LSTM-based Deep Learning Models for non-factoid answer selection

@article{Tan2015LSTMbasedDL,
  title={LSTM-based Deep Learning Models for non-factoid answer selection},
  author={Ming Tan and Bing Xiang and Bowen Zhou},
  journal={CoRR},
  year={2015},
  volume={abs/1511.04108}
}
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 is to define a more composite representation for questions and answers by combining… CONTINUE READING
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