We apply a general recurrent neural network (RNN) encoder framework to community question answering (cQA) tasks. Our approach does not rely on any linguistic processing, and can be applied to different languages or domains. Further improvements are observed when we extend the RNN encoders with a neural attention mechanism that encourages reasoning over entire sequences. To deal with practical issues such as data sparsity and imbalanced labels, we apply various techniques such as transfer learning and multitask learning. Our experiments on the SemEval-2016 cQA task show 10 improvement on a MAP score compared to an information retrieval-based approach, and achieve comparable performance to a strong handcrafted feature-based method.
Very recent work on answer selection also involved the use of neural networks. @cite_8 used LSTM to construct a joint vector based on both the question and the answer and then converted it into a learning to rank problem. @cite_7 proposed several convolutional neural network (CNN) architectures for cQA. Our method differs in that RNN encoder is applied here and by adding attention mechanism we jointly learn which words in question to focus and hence available to conduct qualitative analysis. During classification, we feed the extracted vector into a feed-forward neural network directly instead of using mean max pooling on top of each time steps.
@cite_7 @cite_8
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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. In this paper, we present an approach that address the answer sentence selection problem for question answering. The proposed method uses a stacked bidirectional Long-Short Term Memory (BLSTM) network to sequentially read words from question and answer sentences, and then outputs their relevance scores. Unlike prior work, this approach does not require any syntactic parsing or external knowledge resources such as WordNet which may not be available in some domains or languages. The full system is based on a combination of the stacked BLSTM relevance model and keywords matching. The results of our experiments on a public benchmark dataset from TREC show that our system outperforms previous work which requires syntactic features and external knowledge resources.