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Augmenting Paraphrase Generation with Syntax Information using Graph Convolutional Networks
Version 1
: Received: 30 March 2021 / Approved: 31 March 2021 / Online: 31 March 2021 (07:57:56 CEST)
A peer-reviewed article of this Preprint also exists.
Chi, X.; Xiang, Y. Augmenting Paraphrase Generation with Syntax Information Using Graph Convolutional Networks. Entropy 2021, 23, 566. Chi, X.; Xiang, Y. Augmenting Paraphrase Generation with Syntax Information Using Graph Convolutional Networks. Entropy 2021, 23, 566.
Abstract
Paraphrase generation is an important yet challenging task in NLP. Neural network-based approaches have achieved remarkable success in sequence-to-sequence(seq2seq) learning. Previous paraphrase generation work generally ignores syntactic information regardless of its availability, with the assumption that neural nets could learn such linguistic knowledge implicitly. In this work we make an endeavor to probe into the efficacy of explicit syntactic information for the task of paraphrase generation. Syntactic information can appear in the form of dependency trees which could be easily acquired from off-the-shelf syntactic parsers. Such tree structures could be conveniently encoded via graph convolutional networks(GCNs) to obtain more meaningful sentence representations, which could improve generated paraphrases. Through extensive experiments on four paraphrase datasets with different sizes and genres, we demonstrate the utility of syntactic information in neural paraphrase generation under the framework of seq2seq modeling. Specifically, our GCN-enhanced models consistently outperform their syntax-agnostic counterparts in multiple evaluation metrics.
Keywords
paraphrase generation; syntax information; Graph Convolutional Network; sequence-to-sequence
Subject
Computer Science and Mathematics, Computer Vision and Graphics
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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