Transformer-GCRF: Recovering Chinese Dropped Pronouns with General Conditional Random Fields

Jingxuan Yang, Kerui Xu, Jun Xu, Si Li, Sheng Gao, Jun Guo, Ji-Rong Wen, Nianwen Xue


Abstract
Pronouns are often dropped in Chinese conversations and recovering the dropped pronouns is important for NLP applications such as Machine Translation. Existing approaches usually formulate this as a sequence labeling task of predicting whether there is a dropped pronoun before each token and its type. Each utterance is considered to be a sequence and labeled independently. Although these approaches have shown promise, labeling each utterance independently ignores the dependencies between pronouns in neighboring utterances. Modeling these dependencies is critical to improving the performance of dropped pronoun recovery. In this paper, we present a novel framework that combines the strength of Transformer network with General Conditional Random Fields (GCRF) to model the dependencies between pronouns in neighboring utterances. Results on three Chinese conversation datasets show that the Transformer-GCRF model outperforms the state-of-the-art dropped pronoun recovery models. Exploratory analysis also demonstrates that the GCRF did help to capture the dependencies between pronouns in neighboring utterances, thus contributes to the performance improvements.
Anthology ID:
2020.findings-emnlp.13
Volume:
Findings of the Association for Computational Linguistics: EMNLP 2020
Month:
November
Year:
2020
Address:
Online
Editors:
Trevor Cohn, Yulan He, Yang Liu
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
137–147
Language:
URL:
https://aclanthology.org/2020.findings-emnlp.13
DOI:
10.18653/v1/2020.findings-emnlp.13
Bibkey:
Cite (ACL):
Jingxuan Yang, Kerui Xu, Jun Xu, Si Li, Sheng Gao, Jun Guo, Ji-Rong Wen, and Nianwen Xue. 2020. Transformer-GCRF: Recovering Chinese Dropped Pronouns with General Conditional Random Fields. In Findings of the Association for Computational Linguistics: EMNLP 2020, pages 137–147, Online. Association for Computational Linguistics.
Cite (Informal):
Transformer-GCRF: Recovering Chinese Dropped Pronouns with General Conditional Random Fields (Yang et al., Findings 2020)
Copy Citation:
PDF:
https://aclanthology.org/2020.findings-emnlp.13.pdf
Code
 ningningyang/Transformer-GCRF