UniCOQE: Unified Comparative Opinion Quintuple Extraction As A Set

Zinong Yang, Feng Xu, Jianfei Yu, Rui Xia

Abstract
Comparative Opinion Quintuple Extraction (COQE) aims to identify comparative opinion sentences in product reviews, extract comparative opinion elements in the sentences, and then incorporate them into quintuples. Existing methods decompose the COQE task into multiple primary subtasks and then solve them in a pipeline manner. However, these approaches ignore the intrinsic connection between subtasks and the error propagation among stages. This paper proposes a unified generative model, UniCOQE, to solve the COQE task in one shot. We design a generative template where all the comparative tuples are concatenated as the target output sequence. However, the multiple tuples are inherently not an ordered sequence but an unordered set. The pre-defined order will force the generative model to learn a false order bias and hinge the model’s training. To alleviate this bias, we introduce a new “predict-and-assign” training paradigm that models the golden tuples as a set. Specifically, we utilize a set-matching strategy to find the optimal order of tuples. The experimental results on multiple benchmarks show that our unified generative model significantly outperforms the SOTA method, and ablation experiments prove the effectiveness of the set-matching strategy.
Anthology ID:
2023.findings-acl.775
Volume:
Findings of the Association for Computational Linguistics: ACL 2023
Month:
July
Year:
2023
Address:
Toronto, Canada
Editors:
Anna Rogers, Jordan Boyd-Graber, Naoaki Okazaki
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
12229–12240
Language:
URL:
https://aclanthology.org/2023.findings-acl.775
DOI:
10.18653/v1/2023.findings-acl.775
Bibkey:
Cite (ACL):
Zinong Yang, Feng Xu, Jianfei Yu, and Rui Xia. 2023. UniCOQE: Unified Comparative Opinion Quintuple Extraction As A Set. In Findings of the Association for Computational Linguistics: ACL 2023, pages 12229–12240, Toronto, Canada. Association for Computational Linguistics.
Cite (Informal):
UniCOQE: Unified Comparative Opinion Quintuple Extraction As A Set (Yang et al., Findings 2023)
Copy Citation:
PDF:
https://preview.aclanthology.org/nschneid-patch-5/2023.findings-acl.775.pdf
PDF Search