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Double attention recurrent convolution neural network for answer selection


Bao, Ganchao; Wei, Yuan; Sun, Xin; Zhang, Hongli (2020), Double attention recurrent convolution neural network for answer selection, Dryad, Dataset,


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 multi-layer perceptron (MLP) 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, thereby demonstrating the effectiveness of self-attention, decay self-attention and cross-attention in answer-selection tasks.

Usage Notes

There are a variety of question and answer datasets and code for the answer selection task.

Please see the README file for detailed instructions on how to use each dataset.