Question: Represent the scientific article to find a short TLDR!

Question: The goal of multi-label learning (MLL) is to associate a given instance with its relevant labels from a set of concepts. Previous works of MLL mainly focused on the setting where the concept set is assumed to be fixed, while many real-world applications require introducing new concepts into the set to meet new demands. One common need is to refine the original coarse concepts and split them into finer-grained ones, where the refinement process typically begins with limited labeled data for the finer-grained concepts. To address the need, we propose a special weakly supervised MLL problem that not only focuses on the situation of limited fine-grained supervision but also leverages the hierarchical relationship between the coarse concepts and the fine-grained ones. The problem can be reduced to a multi-label version of negative-unlabeled learning problem using the hierarchical relationship. We tackle the reduced problem with a meta-learning approach that learns to assign pseudo-labels to the unlabeled entries. Experimental demonstrate that our proposed method is able to assign accurate pseudo-labels, and in turn achieves superior classification performance when compared with other existing methods. Multi-label learning (MLL) is an important learning problem with a wide range of applications BID2 BID0 BID11. While traditional setting focuses on the scenario where the label classes are fixed before learning, many real-world applications face different situations. One scenario that is common in many applications is the growing number of classes BID13, where the growth splits high-level concepts to finer-grained ones BID1. For example, the set of classes might start from high-level concepts such as {Animal, . . ., Food}, and then grow to include finer-grained concepts like {Cat, . . ., Dog, . . ., Apple, . . ., Banana}. Typical applications may have collected sufficient number of labeled data for learning the high-level concepts in a fully supervised manner, but it can be challenging for the applications to efficiently adapt the classifier from the high-level (coarse-grained) concepts to the finer-grained ones. Conquering the challenge calls for two components: one is a strategic algorithm to actively collect a few fine-grained and informative labels, and the other is an effective learning model to exploit the fine-grained labels that have been partially collected. This work focuses on the design of the second component-learning an accurate fine-grained classifier with only limited supervision. In particular, we assume that the model receives a data set that contains all the coarse-grained labels and a few fine-grained ones, as shown in FIG0. Then, the problem of constructing a predictive fine-grained model with the presented data set falls under the big umbrella of weakly supervised learning. Specifically, when we focus on leveraging the coarse-grained labels to build a fine-grained classifier, the problem resembles learning with inexact supervision considered by BID12, where the coarse-grained labels are not in the exact form for the desired output and could only provide weak information about the target fine-grained labels. On the other hand, if we focus on using the fine-grained part of the labels to train the classifier, the problem can be viewed as a multi-label variant of learning with incomplete supervision as some instances receive their exact fine-grained ground-truth labels whereas some do not have labels at all BID12. While both the aforementioned problems have attracted much research attention, the combination of them (inexact and incomplete supervision) which our problem of interest can be cast as, has not yet been carefully investigated to the best of our knowledge. Organization In this work, we start from a formal definition of our problem of interest. We then demonstrate a simple way to reduce the original problem into a special form of negative-unlabeled learning problem BID7 leveraging the label hierarchy. To tackle the reduced problem, we begin with a discussion on the caveats carried by some possible existing approaches, and propose a new model that undertakes the challenges posed by inexact and incomplete supervision through a novel learning to learn method which jointly exploits the hierarchical relationship between the coarse-and fine-grained labels, as well as the benefits of all available data in hand. The key idea within our model is to take into account all available information to learn the labeling assignments for the unlabeled entries, called pseudo-labels, and use them to guide the decent direction of the parameter updates on the underlying classifier. Finally, we experimentally demonstrate that the proposed method not only assigns accurate pseudo-labels to the unknown entries but also enjoys significantly better performance than other methods for learning fine-grained classifiers under the limited supervision setting. Formally, we denote an instance by a feature vector x ∈ R d, and its relevant labels by a bit vector y ∈ {−1, 1}K to indicate whether the labels in a pre-defined set Y = {y 1, ..., y K} are relevant, i.e., y[k] = 1 if and only if y k is relevant. In this work, rather than assuming that the set Y is fixed, we consider the problem of splitting the original high-level concepts into finer-grained ones, refining the label set of interest from Y c = {y 1, ..., y C} into Y f = {y 11, y 12, ..., y C1, y C2, ...} as shown in FIG2. Let y c and y f be the corresponding label vectors for Y c and Y f respectively. Assume that we receive a data set DISPLAYFORM0 consisting of N examples that are annotated only with the high-level (coarse-grained) labels, and an additional small set DISPLAYFORM1 of M examples with their fine-grained labels annotated, our goal is to leverage these examples to learn an accurate fine-grained classifier Φ(θ, x): DISPLAYFORM2 K where θ is the model parameter and K is the total number of fine-grained classes. Fully-Supervised Learning A straightforward way to learn a fine-grained classifier is to utilize only the fully-annotated training examples in D tr through standard supervised approaches. Nevertheless, the small number of examples in this set might be unable to train a strong classifier. Moreover, it completely ignores the (weak) supervision provided by the abundant coarse labels. Multi-Label Learning with Missing Labels One way to make use of the higher-level supervision on learning the fine-grained concepts is to leverage the hierarchical relationship where the TAB0 To tackle the reduced problem, one way is to treat the unknown fine-grained labels as missing entries, and apply MLL algorithms that can learn with the presence of missing labels BID4 BID8 BID9. BID9 proposed a classic empirical risk minimization styled method LEML, attempting to solve the optimization problem that arrives at the model parameters which can most accurately recover the observed training labels. Roughly, their objective is formulated as: DISPLAYFORM0 where Ω is the set of indices of the observed entries in Y f and L is a loss function that measures the discrepancy between the predicted and ground-truth labels. From the objective, however, we note that only the observed entries contribute to the learning of model parameters, and the unobserved ones are basically ignored in the model training process. Unfortunately, in our setting, as the observed fine-grained labels are mostly deduced from the irrelevance of their parent labels, LEML is thus unable to exploit the weak supervision provided by the relevant coarse labels. One-Class Multi-Label Learning Another plausible direction to approach the reduced problem is through one-class multi-label learning methods BID10. A common approach took in these methods is to assume the values of the unobserved entries to be the opposite class of the observed ones, and train a cost-sensitive classifier with different weights given to the observed and unobserved entries. Nonetheless, as the underlying ground truths for the missing entries are not necessarily the presumed class, without careful algorithm redesign or label distribution estimation, these methods may suffer from the introduced label bias that in suboptimal performances. While existing solutions have developed different ways of treating the unknown entries during the learning process, they somehow do not delicately exploit the benefits of the unlabeled entries as mentioned in the previous section. In light of this, we seek for a method that could more properly leverage the missing entries with a key assumption that: When the missing entries are all correctly recovered and used in the training process, the classifier learned could achieve the best performance. Based on the assumption, we attempt to find the best labeling assignment to the unknown entries, called pseudo-labels, which when the model is trained accordingly, can lead to best classification performance on the fine-grained concepts. Towards this goal, we propose to use the few examples that receive their fully-annotated fine-grained labels in D tr as a validation set to evaluate the classifier's performance on the fine-grained concepts. Formally, we aim to train our fine-grained classifier using the examples in D tr with a pseudo fine-grained label matrix Y pseudo where: DISPLAYFORM0 where every p ij is a pseudo label to be determined and the objective is: DISPLAYFORM1 Note that with different pseudo-labels assigned to the missing entries, we arrive at different optimal model parameter θ * (Y pseudo). And the optimal assignment of the pseudo-labels should be based on the validation performance of the ing classifier: DISPLAYFORM2 However, solving Eq. 4 to find the optimal pseudo-label assignment requires a computationally prohibiting two-loop optimization procedure. To conquer the optimization challenge, inspired by recent works in meta-learning literature BID3 BID6, we attempt to tackle the problem with an iterative approach which dynamically find the best pseudo-label assignments locally at each optimization step. Specifically, consider a typical gradient descent update step: DISPLAYFORM3 where α is the step size and t is the current timestep. Then, at each iteration t, we aim to learn the pseudo-label assignment which leads to the model parameters that minimize the validation loss after a single update step: DISPLAYFORM4 Solving Eq. 6 at each timestep t could, nevertheless, still be very expensive. As a , we propose a simple approximation of (Y pseudo) * t by looking at the gradient direction (sign) of the validation loss wrt. the pseudo-labels. Particularly, we assign pseudo-labels at timestep t by: DISPLAYFORM5 4 EXPERIMENTS AND DISCUSSIONTo justify the effectiveness of the proposed method, we test our method on a multi-label image dataset MS COCO BID5. We compare our method with three baseline methods, namely, a standard fully-supervised (FS) learning model, LEML, a classic approach in handling typical missing-label setup BID9, and a representative method that tackles the problem of one-class classification (OCC) BID10. We deploy a fully-connected neural network as our underlying base model. In TAB0, we show the of different methods with varying size of D tr. It can be seen that our method consistently achieves the best performances across different settings. It is worthwhile to note that although the standard fully-supervised approach does not leverage the partially labeled examples in D tr, it surprisingly outperforms the other two baseline methods in many cases. To investigate the reasons for so, we plot the learning curves of different methods in Figure 3. From the figure, we see that although LEML and OCC achieve comparable, or even better, performances than the fully-supervised approach at the very beginning of the learning process, the two approaches then quickly suffers from overfitting that in the performance drop. For LEML, we conjecture that the performance degrading comes from the overwhelming number of negative entries dominating the learning dynamic. And arguably, the severe overfitting of OCC from the over-simple assumption on the missing entries which brings label bias into the learning objective. Recover rate of our method To understand the benefits of the pseudo-labels learned in our approach, we show how our method is capable of correctly recovering the missing entries, as well as the correlation between the recover rate and model performance. In Figure 4, we plot the recover performance of the learned pseudo-labels measured by F1-loss (1 − F1-score), and the horizontal bars are the corresponding F1-loss by simply treating all missing entries as ones and assigning them random labels. We can see from the figure that the pseudo-labels learned from our method could much more correctly recover the missing entries than the two naive baselines. In addition, there is a strong correlation between the recover rate and model classification performance. With more accurate assignment of pseudo-labels on the unknown entries, the trained model is able to achieve stronger classification performance. We design a tailored method through a meta-learning strategy, which learns to accurately assign pseudo-labels to the unknown entries of a special weakly supervised MLL problem. Experimental show that our proposed method not only assigns accurate pseudo-labels, but also enable the underlying classifier learned to perform better than other possible existing solutions.

We propose a special weakly-supervised multi-label learning problem along with a newly tailored algorithm that learns the underlying classifier by learning to assign pseudo-labels.


Question: represent the scientific article to find a short tldr.

Question: We develop a reinforcement learning based search assistant which can assist users through a set of actions and sequence of interactions to enable them realize their intent. Our approach caters to subjective search where the user is seeking digital assets such as images which is fundamentally different from the tasks which have objective and limited search modalities. Labeled conversational data is generally not available in such search tasks and training the agent through human interactions can be time consuming. We propose a stochastic virtual user which impersonates a real user and can be used to sample user behavior efficiently to train the agent which accelerates the bootstrapping of the agent. We develop A3C algorithm based context preserving architecture which enables the agent to provide contextual assistance to the user. We compare the A3C agent with Q-learning and evaluate its performance on average rewards and state values it obtains with the virtual user in validation episodes. Our experiments show that the agent learns to achieve higher rewards and better states. Within the domain of search, the recent advances have focused on personalizing the search through recommendations BID27 BID19. While the quality of recommendations have improved, the conventional search interface has not innovated much to incorporate useful contextual cues which are often missed. Conventional search interface enables the end user to perform a keyword based faceted search where the typical work flow goes as follows: the end user types in her search query, applies some filters and then modifies the query based on the . This iterative interaction naturally paves way for incorporating conversations in the process. Instead of the search engine just retrieving the best set, it can interact with the user to collect more contextual cues. For example, if a user searches for birthday gift, the search engine could follow-up by asking who are you buying the gift for. Such information and interaction can provide more humanlike and engaging search experience along with assisting user in discovering their search intent. In this work we address this problem by developing a Reinforcement Learning (RL) BID21 based conversational search agent which interacts with the users to help them in narrowing down to relevant search by providing them contextual assistance. RL based dialogue agents have been designed for tasks like restaurant, bus and hotel reservation BID18 which have limited and well-defined objective search modalities without much scope for subjective discussion. For instance, when searching for a restaurant, the user can specify her preferences (budget, distance, cuisines etc) due to which the problem can be modeled as a slot filling exercise. In contrast, suppose a designer is searching for digital assets (over a repository of images, videos etc) to be used in a movie poster. She would start with a broad idea and her idea would get refined as the search progresses. The modified search intent involves an implicit cognitive feedback which can be used to improve the search . We model our agent for this type of search task. Since the user preferences can not be modeled using a fixed set of facets, we end up with a very large search space which is not the case with most other goal oriented RL agents. We model the search process as a sequence of alternate interactions between the user and the RL agent. The extent to which the RL agent could help the user depends on the sequence and the type of actions it takes according to user behavior. Under the RL framework, intermediate rewards is given to the agent at each step based on its actions and state of conversational search. It learns Since true conversational data is not easily available in search domain, we propose to use query and session log data to develop a stochastic virtual user environment to simulate training episodes and bootstrap the learning of the agent. Our agent interacts with the user to gauge user intent and treats the search engine as a black box service which makes it easily deployable over any search engine. We perform qualitative experiments by simulating validation episodes with different reinforcement learning algorithms under various formulations of the state space to evaluate the performance of the trained agent. Our contributions are three-fold: 1) formulating conversational interactive search as a reinforcement learning problem and proposing a generic and easily extendable set of states, actions and rewards; 2) developing a stochastic user model which can be used to efficiently sample user actions while simulating an episode; 3) we develop A3C (Asynchronous Advantage Actor-Critic) BID15 algorithm based architecture to predict the policy and state value functions of RL agent and compare it with other RL algorithms over performance on validation episodes. There have been various attempts at modeling conversational agents, as dialogue systems BID4 BID31 BID23 BID12 and text-based chat bots BID5 BID13 b; BID24 BID28. Some of these have focused on modeling goal driven RL agent such as indoor way finding system BID5 to assist humans to navigate to their destination and visual input agents which learn to navigate and search an object in a 3-D environment space BID32. Domain independent RL based dialogue systems have been explored in the past. For example, BID23 uses User Satisfaction (US) as the sole criteria to reward the learning agent and completely disregards Task Success(TS). But US is a subjective metric and is much harder to measure or annotate real data with. In our formulation, we provide a reward for task success at the end of search along with extrinsic and auxiliary rewards at intermediate steps (discussed in section 3.4). Other RL based information seeking agents extract information from the environment by sequentially asserting questions but these have not been designed on search tasks involving human interaction and behavior BID1.Neural Conversation Model, based on the SEQ2SEQ framework, uses an end-to-end and domain independent encoder-decoder architecture to maximize the likelihood of next utterance given the previous utterances BID24. The ing model generates grammatically correct sentences though they tend to be repetitive, less engaging and lack consistency such that it cannot perform coherent and meaningful conversation with real humans. To overcome these issues, deep RL has been combined with Neural Conversation Model to foster sustained conversations based on the long-term success of dialogues BID14. The model is initialized with MLE parameters and tuned further using policy gradient BID22 through rewards which capture ease of answering, information flow and semantic coherence. Neural conversation models initialized with MLE parameters have also been improved using batch policy gradient method which efficiently uses labeled data comprising of scores assigned to each dialogue in the conversation BID10. These models require labeled conversational data which is not available for the subjective search tasks we discussed. Our model assists the user at different steps in the search through a set of assist actions instead. RL has also been used for improving document retrieval through query reformulation where the agent sequentially reformulates a given complex query provided by the user BID17 BID16. But their work focuses on single turn episodes where the model augments the given query by adding new keywords. In contrast, our agent engages the user directly into the search which comprises of sequence of alternate turns between user and agent with more degrees of freedom (in terms of different actions the agent can take).To minimize human intervention while providing input for training such agents in spoken dialogue systems, simulated speech outputs have been used to bypass spoken language unit BID4. The system uses word based features obtained from raw text dialogues to represent the state of the Reinforcement Learning Agent. This approach enables to reduce the system's dependence on hand engineered features. User models for simulating user responses have been obtained by using LSTM which learns inter-turn dependency between the user actions. They take as input multiple user dialogue contexts and outputs dialogue acts taking into account history of previous dialogue acts and dependence on the domain BID0.Another prominent research area, closely related to conversational agents, is that of question answering. In case of machine comprehension, the task is to reason over multiple statements to formulate the answer to a given query. Memory Networks BID28 use memories to store information (facts and contexts) internally in a compressed form for later use. RL has been used in visual tasks BID20 BID6 where given an image, history of the utterances and a follow-up question about the image, the agent is trained to answer the question. Unlike machine comprehension based question answering tasks, Visual Dialog tasks are closer to conversational agents as they require the model to maintain and employ the context of the conversation when formulating the replies. But unlike Visual Dialog, our responses are not grounded in the given image and require knowledge beyond the immediate context of the conversation. Often task oriented dialogue systems are difficult to train due to absence of real conversations and subjectivity involved in measuring shortcomings and success of a dialogue BID8. Evaluation becomes much more complex for subjective search systems due to absence of any label which tells whether the intended task had been completed or not. We evaluate our system through rewards obtained while interacting with the user model and also on various real world metrics (discussed in experiments section) through human evaluation. In this paper, we experiment with two different RL algorithms -the Asynchronous Advantage Actor Critic (A3C) algorithm BID15 and the Q-learning BID29. We first discuss preliminaries of RL, then provide the details of the action-state spaces, rewards and virtual user we modeled followed by discussion of the above algorithms and our architecture. Reinforcement Learning is the paradigm to train an agent to operate in an environment E. The agent interacts with the environment in a series of independent episodes and each episode comprises of a sequence of turns. At each turn, the agent observes the state s of the environment (s ∈ S, where S is defined as the state space -the set of possible states) and performs an action a (a ∈ A, where A is defined as the action space -the set of all the possible actions). When the agent performs an action, the state of the environment changes and the agent gets the corresponding reward BID21 ). An optimal policy maximizes cumulative reward that the agent gets based on the actions taken according to the policy from start till the final terminal state is reached in the episode. Action space A is designed to enable the search agent to interact with the user and help her in searching the desired assets conveniently. The agent actions can be divided into two sets -the set of probe intent actions -P and general actions -G as described in TAB0 respectively. The agent uses the probe intent actions P to explicitly query the user to learn more about her context. For instance, the user may make a very open-ended query ing in a diverse set of even though none of them is a good match. In such scenarios, the agent may prompt the user to refine her query or add some other details like where the search would be used. Alternatively, the agent may cluster the search and prompt the user to choose from the clustered categories. These actions serve two purposes -they carry the conversation further and they provide various cues about the search context which is not evident from the search query provided by the user. show display corresponding to most recent user query add to cart suggest user to bookmark assets for later reference ask to download suggest user to download some if they suit her requirement ask to purchase advise the user to buy some paid assets provide discount offer special discounts to the user based on search history sign up ask the user to create an account to receive updates regarding her search ask for feedback take feedback about the search so far provide help list possible ways in which the agent can assist the user salutation greet the user at the beginning; say goodbye when user concludes the searchThe set G consists of generic actions like displaying assets retrieved corresponding to the user query, providing help to the user etc. While probe intent actions are useful to gauge user intent, set G comprises of actions for carrying out the functionality which the conventional search interface provides like "presenting search ". We also include actions which promote the business use cases (such as prompting the user to signup with her email, purchase assets etc). The agent is rewarded appropriately for such prompts depending on the subsequent user actions. Our experiments show that the agent learns to perform different actions at appropriate time steps in search episodes. We model the state representation in order to encapsulate facets of both search and conversation. The state s at every turn in the conversation is modeled using the history of user actionshistory user, 1 history of agent actions -history agent, discretized relevance scores of search -score and a variable length conv which represents number of user responses in the conversation till that point. The variables history user and history agent comprises of user and agent actions in last k turns of the conversational search respectively. This enables us to capture the context of the conversation (in terms of sequence of actions taken). Each user-action is represented as one-hot vector of length 9 (which is the number of unique user actions). Similarly, each agent-action has been represented as a one-hot vector of length 12. The history of the last 10 user and agent actions is represented as concatenation of these one-hot vectors. We use vectors with zero padding wherever needed such as when current history comprises of less than 10 turns. The variable score quantifies the degree of similarity between most recent query and the top 10 most relevant search assets retrieved. They have been taken in state representation to incorporate the dependency between the relevance of probe intent actions and quality of search retrieved. Similarly, length conv has been included since appropriateness of other agent actions like sign up may depend on the duration for which the user has been searching. Reinforcement Learning is concerned with training an agent in order to maximize some notion of cumulative reward. In general, the action taken at time t involves a long term versus short term reward trade-off leading to the classic exploration-exploitation problem. This problem manifests itself even more severely in the context of conversational search. For instance, let us say that the user searches for "nature". Since the user explicitly searched for something, it would seem logical that the most optimal action is to provide the search to the user. Alternatively, instead of going for immediate reward, the agent could further ask the user if she is looking for "posters" or "portraits" which would help in narrowing down the search in the long run. Determining the most optimal action at any point of the conversation is a non-trivial task which highlights the importance of reward modeling. Since we aim to optimize dialogue strategy and do not generate dialogue utterances, we assign the rewards corresponding to the appropriateness of the action performed by the agent considering the state and history of the search. We have used some rewards such as task success (based on implicit and explicit feedback from the user during the search) which is also used in PARADISE framework BID25. At the same time several metrics used by the PARADISE cannot be used for evaluating our system or modeling rewards. For instance, time required (number of turns) for user to search desired cannot be penalized since it can be possible that user is finding the system engaging and helpful in refining the better which may increase number of turns in the search. We model the total reward which the agent gets in one complete dialogue comprises of three kinds of rewards and can be expressed in the form of following equation: DISPLAYFORM0 First kind of reward (r T C) is based on the completion of the task (Task Completion TC) which is download and purchase in the case of our search problem. This reward is provided once at the end of the episode depending on whether the task is completed or not. As second kind of rewards, we provide instantaneous extrinsic rewards BID7 -(r extrinsic) based on the response that the user gives subsequent to an agent action. Rewards provided on the basis of interaction with simulated user have been studied and compared with inverse RL previously BID3. We categorize the user action into three feedback categories, namely good, average or bad. For example, if the agent prompts the user to refine the query and the user does follow the prompt, the agent gets a high reward because the user played along with the agent while if the user refuses, a low reward is given to the agent. A moderate reward will be given if the user herself refines the query without the agent's prompt. Depending on these feedback categories, r extrinsic is provided at every time step in the search. Apart from the extrinsic rewards, we define a set of auxiliary tasks T A specific to the search problem which can be used to provide additional reward signals, r auxiliary, using the environment. We define T A = {# click , # add to cart, # cluster category click, if sign up option exercised}. r auxiliary is determined and provided at every turn in the search based on the values of different auxiliary tasks metrics defined in T A till that turn in the search. Such rewards promotes a policy which improves the performance on these tasks. The RL agent is trained to learn the optimal action policy which requires actual conversational search data which is not available as conversational agents have not been used in the context of search task we defined. To bypass this issue and bootstrap training, we propose a user model that simulates user behavior to interact with the agent during training and validation. Our methodology can be used to model a virtual user using any query and log sessions data. We developed a stochastic environment where the modeled virtual human user responds to agent's actions. The virtual human user has been modeled using query sessions data from a major stock Figure 1: Example of mapping session data to user actions. The session data comprises of sequence of logs, each log comprises of search query, filters applied (content type), offset field and interaction performed by the user (such as search, click etc) photography and digital asset marketplace which contain information on queries made by real users, the corresponding clicks and other interactions with the assets. This information has been used to generate a user which simulates human behavior while searching and converses with the agent during search episode. We map every record in the query log to one of the user actions as depicted in TAB2. Figure 1 shows an example mapping from session data to user action. To model our virtual user, we used the query and session log data of approximately 20 days. The virtual user is modeled as a finite state machine by extracting conditional probabilities -P (U ser Action u| History h of U ser Actions). These probabilities are employed for sampling next user action given the fixed length history of her actions in an episode. The agent performs an action in response to the sampled user action. Subsequent to the action performed by the agent, next user action is sampled which modifies the state and is used to determine the reward the agent gets for its previous action. TAB3 shows a snippet of conditional probability matrix of user actions given the history of last 3 user actions. The query and session log data has been taken from an asset search platform where the marketer can define certain offers/promotions which kick in when the user takes certain actions, for instance the user can be prompted to add some images to cart (via a pop-up box). Users response to such prompts on the search interface is used as proxy to model the effect of RL agent on virtual user's sampled action subsequent to different probe actions by the agent. This ensures that our conditional probability distribution covers the whole probability space of user behavior. In order to incorporate the effect of other agent actions such as sign up which are not present in the query logs, we tweaked the probability distribution realistically in order to bootstrap the agent. The agent can be trained through Q-learning BID26 ) which consists of a real valued function Q: S × A → IR. This Q-function maps every state-action pair (s, a) to a Q-value which is a numerical measure of the expected cumulative reward the agent gets by performing a in state s. Suppose the agent takes an action a in state s such that the environment makes a transition to a state s, the Q-value for the pair (s, a) is updated as follows: DISPLAYFORM0 where α is the learning rate, r is the immediate reward for performing action a in state s in i th user turn in the episode. For our case, an episode refers to one complete session of conversational search between the user and the agent. Once the Q-values are learned, given a state, the action with the maximum Q-value is chosen. In order to prevent the agent from always exploiting the best action in a given state, we employ an − greedy exploration policy BID30, 0 < < 1. The size of our state space is of the order of ≈ 10 7. For Q-learning, we use the table storage method where the Q-values for each state is stored in a lookup table which is updated at every step in a training episode. In this algorithm, we maintain a value function V π and a stochastic policy π as a function of the state. The policy π: A × S → IR defines a probability distribution π(a|s) over the set of actions which the agent may take in state s and is used to sample agent action given the state. The value function V π: S → IR represents the expected cumulative reward from current time step in an episode if policy π is followed after observing state s i.e. V π (s) = IE a∼π(.|s) [Q π (s, a)]. We propose a neural architecture (figure 2) which preserves the context of the conversational search for approximating the policy and value functions. The architecture comprises of a LSTM BID9 which processes the state at a time step t (input i t = s t) and generates an embedding h t which is further processed through a fully connected layer to predict the probability distribution over different actions using softmax function BID2 ) and the value of the input state. Following equations describes our architecture. DISPLAYFORM0, where w LSTM represents the parameters of the LSTM, θ p and θ v represents the set of parameters of the last fully connected layer which outputs the policy p and value v st of the input state s t respectively. We represent all the parameters by θ = {w LSTM, θ p, θ v}. The LSTM state is reset to zero vectors at the start of a search episode. At time-step t in the search episode, the state s t is given as input to the model. The cell and hidden state (c t−1, h t−1) of the LSTM is maintained based on the previous states (s 0, s 1, ..., s t−1) which have already been processed. The LSTM unit remembers the previous states which enables our model to capture the effect of observed states in the search while predicting the probability of different actions. This "memory" implicitly allows our agent to make the next prediction based on the transitions and user behavior observed so far allowing it to mimic the strategy of a real agent assisting the user. The parameters are tuned by optimizing the loss function loss total which can be decomposed into three types of losses.loss total (θ) = loss policy (θ) + loss value (θ) + loss entropy (θ) Figure 2: A3C architecture for predicting policy p t and value V (s t). Current search state s t is processed by a LSTM followed by a fully connected layer. The cell state c t and hidden state h t of LSTM from previous time step is retained while processing the next state during an episode. The same fully connected layer is used for prediction at different time steps in an episode. The episode terminates at time step T.We now explain all the three losses. In A3C algorithm, the agent is allowed to interact with the environment to roll-out an episode. The network parameters are updated after completion of every n-steps in the roll-out. An n-step roll-out when the current state is s t can be expressed as (s t, a t, r t, s t+1, v st) → (s t+1, a t+1, r t+1, s t+1, v st+1) →... → (s t+n−1, a t+n−1, r t+n−1, s t+n, v st+n−1). We also calculate V (s t+n ; θ) in order to estimate loss value which is defined as: DISPLAYFORM1 Thus an n-step roll-out allows us to estimate the target value of a given state using the actual rewards realized and value of the last state observed at the end of the roll-out. Value of a terminal state s T is defined as 0. Each roll-out yields n samples to train the network on the value loss function using these estimated values. In a similar way, the network is trained on loss policy which is defined as: DISPLAYFORM2 The above loss function tunes the parameter in order to shift the policy in favor of actions which provides better advantage A(a t, s t, θ) given the state s t. This advantage can be interpreted as additional reward the agent gets by taking action a t in state s t over the average value of the state V (s t ; θ) as the reference. However, this may bias the agent towards a particular or few actions due to which the agent may not explore other actions in a given state. To prevent this, we add entropy loss to the total loss function which aims at maximizing the entropy of probability distribution over actions in a state.loss entropy (θ) = − a∈A −p(a|s i ; θ) log(p(a|s i ; θ)), for i = t, t + 1,..., t + n − 1The total loss function loss total incorporates exploitation-exploration balance through policy and entropy loss functions optimization. The value function V π (s) is used for determining value of a state to be used as reference while determining advantage of different actions in loss policy. We use Adam optimizer BID11 for optimizing the loss function on model parameters θ. In order to improve the exploration capacity of the final agent trained, A3C comprises of a global model and uses multiple asynchronous agents which interact with their own copy of environment in parallel. Each agent uses its local gradients of the loss function with respect to model parameters to update the parameters of the global model and then copies the parameters of the global model for subsequent training. This is repeated after completion of every fixed number of episodes for each agent which in faster convergence. Including the vectors which encode the history of agent and user actions in last k turns of the search to the state captures the local context. User behavior at current time-step can be affected by queries far away in the history. Since the search episode may extend indefinitely, local context is not sufficient to capture this behavior. The LSTM unit in our architecture aggregates the local context as it sequentially processes the states in an episode into a global context which in capturing context at a global search level. In this section, we evaluate the trained agent with the virtual user model and discuss the obtained with the two reinforcement learning techniques, A3C and Q-learning, and compare them. For each algorithm, we simulate validation episodes after each training episode and plot the average rewards and mean value of the states obtained during the validation episodes. We also developed a chat-search interface (implementation details in section 6.1 in the appendix) where real users can interact with the trained agent during their search. 2 Some conversations between the trained agent and real humans using this interface have been provided in section 6.2 in the appendix. The global model is obtained using 10 local agents which are trained in parallel threads (each trained over 350 episodes). We compare the validation using this global model for different state representations for conversational search and hyper-parameter settings such as discount factor (γ) (which affects exploration vs exploitation trade-off) and the LSTM size which controls the context preserving capacity of our architecture. We experiment with 3 values of discount factor and fix the LSTM size to 250. Figure 3 shows the validation trend in average rewards corresponding to 3 discount factors. Greater discount factor (lower value of γ) in lowers weights for the future rewards. With a large discount factor, the agent tries to maximize the immediate rewards by taking the greedy actions since future rewards are discounted to a larger extent. We validate this by computing the variance in the for each case. We do not consider the values before 100 episodes as the network is under-fitting in that region. The variance values for the 3 cases (γ = 0.90, 0.70, 0.60) are 1.5267, 1.627, and 1.725 respectively. Since the agent takes more greedy actions with higher discount factors, the variance in the reward values also increases since the greedy approach yields good rewards in some episodes and bad rewards in others. Figure 3: Plot of average validation reward against number of training episodes for A3C agent. The size of LSTM is 250 for each plot with varying discount factor; γ = 0.90 (left), γ = 0.80 (middle) and γ = 0.60 (right). It can be observed that a lower γ value in higher variance in the rewards ing in a greedy (less exploratory) policy. For further experiments, we fix the discount value to 0.90 as this value achieves better explorationexploitation balance. In the next setup, we vary the size of the LSTM as 100, 150 and 250 to determine the effect of size of the context preserved. FIG0 depicts the trend in mean value of states observed in an episode. We observe that larger size of the LSTM in better states which the agent observes on an average since average state value is higher. This demonstrates that a bigger LSTM size providing better capacity to remember the context in agent performing actions which yield improved states in search. In this experiment, we model the state vector without incorporating the action history vectorshistory user and history agent. In FIG1, we plot the mean state values observed in an episode and compare the case where the history of actions is added to state vector with the case where it is not added. The 3 plots correspond to different LSTM sizes. For large LSTM size (= 250), the history need not be explicitly added to the state as the LSTM is able to preserve the context and eventually achieves the same mean state values. But if the LSTM does not have enough capacity, as in case of LSTM size = 100, the mean state values observed with history vector included in the state is more than when it is not included. This demonstrates that including the local context in state representation is useful to enable the architecture to aggregate it into global context. FIG2 shows the trend in average reward in validation episode versus number of training episodes. We experimented with values of different hyper-parameters for Q-learning such as discount (γ) and exploration control parameter determined their optimal values to be 0.70 and 0.90 respectively based on trends in the validation curve and average reward value at convergence. We compare the A3C agent (with LSTM size 250 and γ = 0.90 (left plot in figure 3) ) with the Q-learning agent (figure 6). It can be observed that the A3C agent is able to obtain better averaged awards (≈ 2.0) in validation episodes upon convergence as compared to the Q-agent which obtains ≈ 0.50. Since A3C algorithm performs and generalize better than Q-learning approach, we evaluated our system trained using A3C algorithm in the rebuttal period through professional designers who regularly use image search site for their design tasks. To evaluate the effectiveness of our system when interacting with professional designers, we asked them to search images which they will use while designing a poster on natural scenery using both our conversational search agent and conventional search interface provided by stock photography marketplace. We collected feedback, from 12 designers, for both the search modalities across the different set of tasks. We asked the designers to rate our conversational search system on following metrics. TAB4 shows average rating value of each of these metrics.1. Information flow to measure the extent to which the agent provide new information and suggestions which helped in driving the search forward (on a scale of 1 to 5 where 5 represents high information flow). 2. Appropriateness of actions to measure the suitability of actions taken by the agent during the search in terms of coherence (on a scale of 1 to 5 where 5 represents high appropriateness and denotes that it took right actions at right time during the search). 3. Repetitiveness to measure how repetitive was the agents actions in providing assistance during their search (on a scale of 1-5 where 1 represents not repetitive at all). In addition to the above metrics, we also asked the designers to compare our system to conventional search interface of the stock photography marketplace in terms of following metrics:1. Engagement: This is to measure how interactive and engaging conversational search is compared to conventional search on scale of 1 to 5 where 5 represents that conversational search is much more engaging and 1 represents same engagement as conventional search. We averaged the rating provided by the designers. Our system could achieve an average rating of 2.67 in this metric. We asked the designers to compare the two search modalities in terms of time required to search and reach to desired search . We asked them to choose one of three options -conversational search required, 1.more time, 2. About the same time, 3. Less time (faster), than conventional search. About 33.3% of designers said that it requires more time while 16.7% said that conversational search reduced the time required to search the desired . The remaining 50% believed that it required about the same time. 3. Ease of using conversational search compared to conventional search: We asked them to choose one of three options -conversational search is, 1. Difficult to use and adds additional burden, 2. About the same to use, 3. Much easier to use, compared to conventional search. 33.3% of the designers believed that conversational search is easier than conventional search, 41.7% said that it is same as conventional search while 25% believed that it is difficult to perform conversational search than conventional search. The above evaluation shows that although we trained the bootstrapped agent through user model, it performs decently well with actual users by driving their search forward with appropriate actions without being much repetitive. The comparison with the conventional search shows that conversational search is more engaging. In terms of search time, it ed in more search time for some designers while it reduces overall time required to search the desired in some cases, in majority cases it required about the same time. The designers are regular users of conventional search interface and well versed with it, even then majority of them did not face any cognitive load while using our system with 33.3% of them believing that it is easier than conventional search. In this paper, we develop a Reinforcement Learning based search assistant to interact with customers to help them search digital assets suited to their use-case. We model the rewards, state space, action space and develop an A3C based architecture which leverages the context of search to predict the policy. The trained agent is able to obtain higher average rewards in the validation episodes with virtual user and observes states with better values indicative of providing better search experience. We also propose a virtual stochastic user model to interact and train the RL agent in absence of labeled conversational data which accelerates the process of obtaining a bootstrapped agent. As the next step, we would deploy our system to collect true conversational data which can be used to fine tune the current model as well as to train a new model which can generate the natural language responses in addition to deciding the action. In different search domains, designing the state and action space can take significant time which makes every situation an absolutely new task to be solved. To approach this issue as a future work, another system can be designed which helps in the automation of state space characterization with the help of system query logs. Chat interface comprises of a two-pane window, one for text dialogues and other for viewing search . The chat interface allows the user to convey queries in form of dialogue in an unrestricted format. The user message is parsed by NLU which deduces user action from the input message. The NLU additionally obtains and redirects the query to search engine if the user action is new query or ref ine query. User action and search engine output are forwarded to RL agent by NLU. The RL agent performs an action according to the learned policy based on which a formatted response is displayed on chat interface by the NLU. In addition to inputting the message, the chat interface provides functionality for other user actions such as liking a search , adding assets to cart etc. The NLU is a rule based unit which utilizes dependencies between words and their POS tags in a sentence to obtain query and user action. 3 The query is formulated sequentially by locating the primary keyword and then determining the surrounding keywords. A distinction between the user actions new query and ref ine query is made depending on whether the current primary keyword is present in previous queries. A database is used to store the user queries to incorporate context of conversation while formulating the search query to be sent to the search engine. Similarly rules and stop words are used for determining different user actions. For instance, words like'yes/'no' are used for providing feedback signals when the agent asks different probing questions, words like'more' means that the user is requesting more for the same query,'cluster category click' determined based on whether the user clicks on one of the categorical options provided by the agent,'search similar' is determined on the basis of whether the user drags the url of one of the search into the text area. The search engine seeks assets from asset database corresponding to the search query formulated by NLU. The asset database comprises of about 5 million assets of different types like images, videos etc. Assets are indexed in asset database using tags and other metadata for retrieval during the search. 4. The search engine also fetches and clusters assets having tags similar to most relevant assets retrieved to formulate related categorical asset options. Relevance scores of retrieved assets are calculated on the basis of the degree of match between asset metadata and the query. These scores are dispatched with fetched assets and categorical options to the NLU. User: hello Agent: Hello, how may I help you? User: i want images of cars Agent: Categories might help you get better responses, click on the options below -sporty cars, expensive cars, city cars, sedan cars User: city cars Agent: Here are some of the images User: show more Agent: Do you want to browse through the following options? -urban city cars, wrecked city cars, traffic city cars User: urban city cars Agent: Your cart is the place where you can add the images you like. Click on the add to cart icon User: <User adds few assets to cart> Agent: Could you refine your query further so I can get you better images? User: racing Agent: Your cart is the place where you can add the images you like. Click on the add to cart icon User: <User drags an asset into text area to search for similar ones> Agent: What are you going to use the images for? User: i am organizing a racing competition Agent: Here you go, these are some of the best matches for your query User: <User adds few assets to cart> Agent: Would you like to add something to your query? User: no, bye Example 2 Agent: Hi, type in the box below User: show some cpu Agent: Here are some of the images User: can you show some more Agent: Check out some images that we have User: <User drags an asset into text area to search for similar ones> Agent: Where will you use these images? User: in my mac Agent: Results for your query User: <User adds few assets to cart> Agent: I can sign you up, search images for you, add them to your cart and much more. Type in the box to chat User: show me more Agent: Is there anything else you want to search? User: yes Agent: We also have many images in these categories -Computer golden cpu, Technology golden cpu, Internet golden cpu, Abstract golden cpu, Chip golden cpu User: internet golden cpu Agent: Want me to get you anything else? User: no, thanks for the help. Agent: You may enter your search query User: show me some windows Agent: Check out the images User: more Agent: Here are some of the images User: more Agent: Check out some images that we have User: <User search for assets similar to one of the search displayed> Agent: Refine your query further to get better User: reflecting windows Agent: Would you like to add something to your collections now? You can simply click on the add to cart icon User: <User adds few assets to cart> Agent: Could you refine your query further so I can get you better images? User: shining reflecting windows Agent: You could add the images to your cart for better exploration, click on the cart icon User: no Agent: Where will you use these images? User: i will use them for construction designs Agent: Here you go, these are some of the best matches for your query User: show some images of mountains Agent: We also have many images in these categories, should I show you some?User: nature mountains Agent: Where will you use these images? User: i am organizing adventure sports Agent: Check out the images User: can you show some more Agent: Images can be added to cart for building a separate collection of stuff that you like. You can simply click on add to cart button User: <User adds few assets to cart> Agent: Want me to get you anything else? User: i want people riding bike on mountains Agent: We also have many images in these categories, should I show you some? -high mountains, hiking sports mountains, mountaineering mountains User: hiking sports mountains Agent: Signing up takes a moment, just give me your email id User: ABC@XYZ.com

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A Reinforcement Learning based conversational search assistant which provides contextual assistance in subjective search (like digital assets).

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We propose a novel Intrinsically Motivated Goal Exploration architecture with unsupervised learning of goal space representations, and evaluate how various implementations enable the discovery of a diversity of policies.