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Chinese Opinion Role Labeling with Corpus Translation: A Pivot Study

  • Ranran Zhen
  • , Rui Wang
  • , Guohong Fu*
  • , Chengguo Lv
  • , Meishan Zhang
  • *Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Opinion Role Labeling (ORL), aiming to identify the key roles of opinion, has received increasing interest. Unlike most of the previous works focusing on the English language, in this paper, we present the first work of Chinese ORL. We construct a Chinese dataset by manually translating and projecting annotations from a standard English MPQA dataset. Then, we investigate the effectiveness of cross-lingual transfer methods, including model transfer and corpus translation. We exploit multilingual BERT with Contextual Parameter Generator and Adapter methods to examine the potentials of unsupervised cross-lingual learning and our experiments and analyses for both bilingual and multilingual transfers establish a foundation for the future research of this task.

Original languageEnglish
Title of host publicationEMNLP 2021 - 2021 Conference on Empirical Methods in Natural Language Processing, Proceedings
PublisherAssociation for Computational Linguistics (ACL)
Pages10139-10149
Number of pages11
ISBN (Electronic)9781955917094
DOIs
StatePublished - 2021
Externally publishedYes
Event2021 Conference on Empirical Methods in Natural Language Processing, EMNLP 2021 - Hybrid, Punta Cana, Dominican Republic
Duration: 7 Nov 202111 Nov 2021

Publication series

NameEMNLP 2021 - 2021 Conference on Empirical Methods in Natural Language Processing, Proceedings

Conference

Conference2021 Conference on Empirical Methods in Natural Language Processing, EMNLP 2021
Country/TerritoryDominican Republic
CityHybrid, Punta Cana
Period7/11/2111/11/21

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