Title: DeepProbe: Information Directed Sequence Understanding and Chatbot Design via Recurrent Neural Networks,
Abstract: Information extraction and user intention identification are central topics
in modern query understanding and recommendation systems. In this paper, we
propose DeepProbe, a generic information-directed interaction framework which
is built around an attention-based sequence to sequence (seq2seq) recurrent
neural network. DeepProbe can rephrase, evaluate, and even actively ask
questions, leveraging the generative ability and likelihood estimation made
possible by seq2seq models. DeepProbe makes decisions based on a derived
uncertainty (entropy) measure conditioned on user inputs, possibly with
multiple rounds of interactions. Three applications, namely a rewritter, a
relevance scorer and a chatbot for ad recommendation, were built around
DeepProbe, with the first two serving as precursory building blocks for the
third. We first use the seq2seq model in DeepProbe to rewrite a user query into
one of standard query form, which is submitted to an ordinary recommendation
system. Secondly, we evaluate DeepProbe's seq2seq model-based relevance
scoring. Finally, we build a chatbot prototype capable of making active user
interactions, which can ask questions that maximize information gain, allowing
for a more efficient user intention idenfication process. We evaluate first two
applications by 1) comparing with baselines by BLEU and AUC, and 2) human judge
evaluation. Both demonstrate significant improvements compared with current
state-of-the-art systems, proving their values as useful tools on their own,
and at the same time laying a good foundation for the ongoing chatbot
application.