2016 LSTMbasedDeepLearningModelsforN

• (Tan et al., 2016) ⇒ Ming Tan, Cicero dos Santos, Bing Xiang, and Bowen Zhou. (2016). “LSTM-based Deep Learning Models for Non-factoid Answer Selection.” In: Proceedings of ICLR 2016 Workshop. eprint arXiv:1511.04108

Subject Headings: Question Answering (QA) System; Neural QA System.

Notes

• added new experiments on TREC-QA.

Cited By

• http://scholar.google.com/scholar?q=%222016%22+LSTM-based+Deep+Learning+Models+for+Non-factoid+Answer+Selection
• https://openreview.net/forum?id=ZY9xwl3PDS5Pk8ELfEzP

Quotes

Author Keywords

• Computer Science - Computation and Language; Computer Science - Machine Learning

Abstract

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.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2016 LSTMbasedDeepLearningModelsforN	Ming Tan Bing Xiang Bowen Zhou Cicero dos Santos			LSTM-based Deep Learning Models for Non-factoid Answer Selection