research-article

Structurally Comparative Hinge Loss for Dependency-Based Neural Text Representation

Authors:

Chengqing ZongAuthors Info & Claims

ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP), Volume 19, Issue 4

Article No.: 58, Pages 1 - 19

https://doi.org/10.1145/3387633

Published: 18 May 2020 Publication History

Abstract

Dependency-based graph convolutional networks (DepGCNs) are proven helpful for text representation to handle many natural language tasks. Almost all previous models are trained with cross-entropy (CE) loss, which maximizes the posterior likelihood directly. However, the contribution of dependency structures is not well considered by CE loss. As a result, the performance improvement gained by using the structure information can be narrow due to the failure in learning to rely on this structure information. To face the challenge, we propose the novel structurally comparative hinge (SCH) loss function for DepGCNs. SCH loss aims at enlarging the margin gained by structural representations over non-structural ones. From the perspective of information theory, this is equivalent to improving the conditional mutual information of model decision and structure information given text. Our experimental results on both English and Chinese datasets show that by substituting SCH loss for CE loss on various tasks, for both induced structures and structures from an external parser, performance is improved without additional learnable parameters. Furthermore, the extent to which certain types of examples rely on the dependency structure can be measured directly by the learned margin, which results in better interpretability. In addition, through detailed analysis, we show that this structure margin has a positive correlation with task performance and structure induction of DepGCNs, and SCH loss can help model focus more on the shortest dependency path between entities. We achieve the new state-of-the-art results on TACRED, IMDB, and Zh. Literature datasets, even compared with ensemble and BERT baselines.

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Cited By

Zhao CWu MYang XSun XWang SLi D(2024)Cross-Domain Aspect-based Sentiment Classification with Pre-Training and Fine-Tuning Strategy for Low-Resource DomainsACM Transactions on Asian and Low-Resource Language Information Processing10.1145/3653299Online publication date: 21-Mar-2024
https://doi.org/10.1145/3653299
Zhao XLi XWang QYan T(2021)A family of Hermitian dual-containing constacyclic codes and related quantum codesQuantum Information Processing10.1007/s11128-021-03102-320:5Online publication date: 22-May-2021
https://dl.acm.org/doi/10.1007/s11128-021-03102-3

Index Terms

Structurally Comparative Hinge Loss for Dependency-Based Neural Text Representation
1. Computing methodologies
  1. Artificial intelligence
    1. Natural language processing
2. Information systems
  1. Information retrieval
    1. Document representation

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cover image ACM Transactions on Asian and Low-Resource Language Information Processing

ACM Transactions on Asian and Low-Resource Language Information Processing Volume 19, Issue 4

July 2020

291 pages

ISSN:2375-4699

EISSN:2375-4702

DOI:10.1145/3391538

Editor:
Imed Zitouni
Microsoft, USA

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Publication History

Published: 18 May 2020

Online AM: 07 May 2020

Accepted: 01 March 2020

Revised: 01 December 2019

Received: 01 August 2019

Published in TALLIP Volume 19, Issue 4

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Cited By

Zhao CWu MYang XSun XWang SLi D(2024)Cross-Domain Aspect-based Sentiment Classification with Pre-Training and Fine-Tuning Strategy for Low-Resource DomainsACM Transactions on Asian and Low-Resource Language Information Processing10.1145/3653299Online publication date: 21-Mar-2024
https://doi.org/10.1145/3653299
Zhao XLi XWang QYan T(2021)A family of Hermitian dual-containing constacyclic codes and related quantum codesQuantum Information Processing10.1007/s11128-021-03102-320:5Online publication date: 22-May-2021
https://dl.acm.org/doi/10.1007/s11128-021-03102-3

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