Train Once for All: A Transitional Approach for Efficient Aspect Sentiment Triplet Extraction

ACL ARR 2025 May Submission1993 Authors

Abstract:

Aspect-Opinion Pair Extraction (AOPE) and Aspect Sentiment Triplet Extraction (ASTE) have drawn growing attention in NLP. However, most existing approaches extract aspects and opinions
independently, optionally adding pairwise relations, often leading to error propagation and high time complexity. To address these challenges and being inspired by transition-based dependency parsing, we propose the first transition-based model for AOPE and ASTE that performs aspect and opinion extraction jointly, which also better captures position-aware aspect-opinion relations and mitigates entity-level bias. By integrating contrastive-augmented optimization, our model delivers more accurate action predictions and jointly optimizes separate subtasks in linear time.
Extensive experiments on four commonly used ASTE/AOPE datasets show that,
our proposed transition-based model outperform previous models on two out of the four datasets when trained on a single dataset.
When multiple training sets are used, our proposed method achieves new state-of-the-art results on all datasets. We show that this is partly due to our model's ability to benefit from transition actions learned from multiple datasets and domains.
Our code is available at https://anonymous.4open.science/r/trans_aste-8FCF.

Paper Type: Long

Research Area: Sentiment Analysis, Stylistic Analysis, and Argument Mining

Research Area Keywords: relation extraction, dependency parsing, contrastive learning, structured prediction, data-efficient training

Contribution Types: Model analysis & interpretability, NLP engineering experiment, Approaches to low-resource settings, Approaches low compute settings-efficiency, Publicly available software and/or pre-trained models

Languages Studied: English

Submission Number: 1993