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3 months ago

Multiway Attention Networks for Modeling Sentence Pairs

{Ming Zhou Furu Wei Chuanqi Tan Weifeng Lv Wenhui Wang}

Multiway Attention Networks for Modeling Sentence Pairs

Abstract

Modeling sentence pairs plays the vital role forjudging the relationship between two sentences,such as paraphrase identification, natural languageinference, and answer sentence selection. Previouswork achieves very promising results using neuralnetworks with attention mechanism. In this paper,we propose the multiway attention networks whichemploy multiple attention functions to match sentence pairs under the matching-aggregation framework. Specifically, we design four attention functions to match words in corresponding sentences.Then, we aggregate the matching information fromeach function, and combine the information fromall functions to obtain the final representation. Experimental results demonstrate that the proposedmultiway attention networks improve the result onthe Quora Question Pairs, SNLI, MultiNLI, and answer sentence selection task on the SQuAD dataset.

Benchmarks

BenchmarkMethodologyMetrics
natural-language-inference-on-snli150D Multiway Attention Network Ensemble
% Test Accuracy: 89.4
% Train Accuracy: 95.5
Parameters: 58m
natural-language-inference-on-snli150D Multiway Attention Network
% Test Accuracy: 88.3
% Train Accuracy: 94.5
Parameters: 14m
paraphrase-identification-on-quora-questionMwAN
Accuracy: 89.12

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Multiway Attention Networks for Modeling Sentence Pairs | Papers | HyperAI