Syntax Controlled Knowledge Graph-to-Text Generation with Order and Semantic Consistency

Jin Liu, Chongfeng Fan, Fengyu Zhou, Huijuan Xu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

The knowledge graph (KG) stores a large amount of structural knowledge, while it is not easy for direct human understanding. Knowledge graph-to-text (KG-to-text) generation aims to generate easy-to-understand sentences from the KG, and at the same time, maintains semantic consistency between generated sentences and the KG. Existing KG-to-text generation methods phrase this task as a sequence-tosequence generation task with linearized KG as input and consider the consistency issue of the generated texts and KG through a simple selection between decoded sentence word and KG node word at each time step. However, the linearized KG order is commonly obtained through a heuristic search without data-driven optimization. In this paper, we optimize the knowledge description order prediction under the order supervision extracted from the caption and further enhance the consistency of the generated sentences and KG through syntactic and semantic regularization. We incorporate the Part-of-Speech (POS) syntactic tags to constrain the positions to copy words from the KG and employ a semantic context scoring function to evaluate the semantic fitness for each word in its local context when decoding each word in the generated sentence. Extensive experiments are conducted on two datasets,WebNLG and DART, and achieve state-of-the-art performances. Our code is now public available.

Original languageEnglish (US)
Title of host publicationFindings of the Association for Computational Linguistics
Subtitle of host publicationNAACL 2022 - Findings
PublisherAssociation for Computational Linguistics (ACL)
Pages1278-1291
Number of pages14
ISBN (Electronic)9781955917766
StatePublished - 2022
Event2022 Findings of the Association for Computational Linguistics: NAACL 2022 - Seattle, United States
Duration: Jul 10 2022Jul 15 2022

Publication series

NameFindings of the Association for Computational Linguistics: NAACL 2022 - Findings

Conference

Conference2022 Findings of the Association for Computational Linguistics: NAACL 2022
Country/TerritoryUnited States
CitySeattle
Period7/10/227/15/22

All Science Journal Classification (ASJC) codes

  • Computational Theory and Mathematics
  • Computer Science Applications
  • Information Systems

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