Double attention recurrent convolution neural network for answer selection

Royal Society Open Science
Ganchao BaoHongli Zhang

Abstract

Answer selection is one of the key steps in many question answering (QA) applications. In this paper, a new deep model with two kinds of attention is proposed for answer selection: the double attention recurrent convolution neural network (DARCNN). Double attention means self-attention and cross-attention. The design inspiration of this model came from the transformer in the domain of machine translation. Self-attention can directly calculate dependencies between words regardless of the distance. However, self-attention ignores the distinction between its surrounding words and other words. Thus, we design a decay self-attention that prioritizes local words in a sentence. In addition, cross-attention is established to achieve interaction between question and candidate answer. With the outputs of self-attention and decay self-attention, we can get two kinds of interactive information via cross-attention. Finally, using the feature vectors of the question and answer, elementwise multiplication is used to combine with them and multilayer perceptron is used to predict the matching score. Experimental results on four QA datasets containing Chinese and English show that DARCNN performs better than other answer selection models, thereb...Continue Reading

Associated Datasets

References

Oct 23, 1997·Neural Computation·S Hochreiter, J Schmidhuber

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Citations

Jan 22, 2021·Drug Discovery Today·Saifur R KhanMichael B Wheeler

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Software Mentioned

MP
IABRNN
ABCNN
BiMPM
CNN
DARCNN
MPCNN
BiLSTM
AP
TrecQA

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