DTDA: Dual-channel Triple-to-quintuple Data Augmentation for Comparative Opinion Quintuple Extraction

Abstract

Comparative Opinion Quintuple Extraction (COQE) is an essential task in sentiment analysis that entails the extraction of quintuples from comparative sentences. Each quintuple comprises a subject, an object, a shared aspect for comparison, a comparative opinion and a distinct preference. The prevalent reliance on extensively annotated datasets inherently constrains the efficiency of training. Manual data labeling is both time-consuming and labor-intensive, especially labeling quintuple data. Herein, we propose a Dual-channel Triple-to-quintuple Data Augmentation (DTDA) approach for the COQE task. In particular, we leverage ChatGPT to generate domain-specific triple data. Subsequently, we utilize these generated data and existing Aspect Sentiment Triplet Extraction (ASTE) data for separate preliminary fine-tuning. On this basis, we employ the two fine-tuned triple models for warm-up and construct a dual-channel quintuple model using the unabridged quintuples. We evaluate our approach on three benchmark datasets: Camera-COQE, Car-COQE and Ele-COQE. Our approach exhibits substantial improvements versus pipeline-based, joint, and T5-based baselines. Notably, the DTDA method significantly outperforms the best pipeline method, with exact match $F$1-score increasing by 10.32%, 8.97%, and 10.65% on Camera-COQE, Car-COQE and Ele-COQE, respectively. More importantly, our data augmentation method can adapt to any baselines. When integrated with the current SOTA UniCOQE method, it further improves performance by 0.34%, 1.65%, and 2.22%, respectively. We will make all related models and source code publicly available upon acceptance.