In this paper, we apply a general deep learning (DL) framework for the answer selection task, which does not depend on manually defined features or linguistic tools. The basic framework is to build the embeddings of questions and answers based on bidirectional long short-term memory (biLSTM) models, and measure their closeness by cosine similarity. We further extend this basic model in two directions. One direction is to define a more composite representation for questions and answers by combining convolutional neural network with the basic framework. The other direction is to utilize a simple but efficient attention mechanism in order to generate the answer representation according to the question context. Several variations of models are provided. The models are examined by two datasets, including TREC-QA and InsuranceQA. Experimental results demonstrate that the proposed models substantially outperform several strong baselines.
@cite_6: We apply a general deep learning framework to address the non-factoid question answering task. Our approach does not rely on any linguistic tools and can be applied to different languages or domains. Various architectures are presented and compared. We create and release a QA corpus and setup a new QA task in the insurance domain. Experimental results demonstrate superior performance compared to the baseline methods and various technologies give further improvements. For this highly challenging task, the top-1 accuracy can reach up to 65.3 on a test set, which indicates a great potential for practical use.
The framework proposed in this work belongs to the first category. There are two major differences between our approaches and the work in : (1) The architectures developed in are only based on CNN, whereas our models are based on bidirectional LSTMs, which are more capable of exploiting long-range sequential context information. Moreover, we also integrate the CNN structures on the top of biLSTM for better performance. (2) @cite_6 tackle the question and answer independently, while the proposed structures develop an efficient attentive models to generate answer embeddings according to the question.