Does syntax matter? A strong baseline for Aspect-based Sentiment Analysis with RoBERTa

Junqi Dai, Hang Yan, Tianxiang Sun, Pengfei Liu, Xipeng Qiu


Abstract
Aspect-based Sentiment Analysis (ABSA), aiming at predicting the polarities for aspects, is a fine-grained task in the field of sentiment analysis. Previous work showed syntactic information, e.g. dependency trees, can effectively improve the ABSA performance. Recently, pre-trained models (PTMs) also have shown their effectiveness on ABSA. Therefore, the question naturally arises whether PTMs contain sufficient syntactic information for ABSA so that we can obtain a good ABSA model only based on PTMs. In this paper, we firstly compare the induced trees from PTMs and the dependency parsing trees on several popular models for the ABSA task, showing that the induced tree from fine-tuned RoBERTa (FT-RoBERTa) outperforms the parser-provided tree. The further analysis experiments reveal that the FT-RoBERTa Induced Tree is more sentiment-word-oriented and could benefit the ABSA task. The experiments also show that the pure RoBERTa-based model can outperform or approximate to the previous SOTA performances on six datasets across four languages since it implicitly incorporates the task-oriented syntactic information.
Anthology ID:
2021.naacl-main.146
Volume:
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
Month:
June
Year:
2021
Address:
Online
Editors:
Kristina Toutanova, Anna Rumshisky, Luke Zettlemoyer, Dilek Hakkani-Tur, Iz Beltagy, Steven Bethard, Ryan Cotterell, Tanmoy Chakraborty, Yichao Zhou
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1816–1829
Language:
URL:
https://aclanthology.org/2021.naacl-main.146
DOI:
10.18653/v1/2021.naacl-main.146
Bibkey:
Cite (ACL):
Junqi Dai, Hang Yan, Tianxiang Sun, Pengfei Liu, and Xipeng Qiu. 2021. Does syntax matter? A strong baseline for Aspect-based Sentiment Analysis with RoBERTa. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 1816–1829, Online. Association for Computational Linguistics.
Cite (Informal):
Does syntax matter? A strong baseline for Aspect-based Sentiment Analysis with RoBERTa (Dai et al., NAACL 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.naacl-main.146.pdf
Video:
 https://aclanthology.org/2021.naacl-main.146.mp4
Code
 ROGERDJQ/RoBERTaABSA
Data
SemEval-2014 Task-4