MATO : A Model-Agnostic Training Optimization for Aspect Sentiment Triplet Extraction (2025)

Abstract: As an important fine-grained sentiment analysis task, aspect sentiment triplet extraction (ASTE) aims to identify three elements, i.e., aspect, opinion and sentiment polarity as a triplet. Advanced ASTE researches have mostly explored triplet-wise ability to achieve superior improvement. However, existing models with strong in-house performances may struggle to generalize to the challenging cases with the diverse expression of inter-triplet and intra-triplet elements. To this end, we propose a **M**odel-**A**gnostic **T**raining **O**ptimization (**MATO**) to improve ASTE model inference consistent with expected results facing triplet element diversity. Specifically, we design inter-triplet and intra-triplet metamorphic relations (MRs), and calculate the violation rate (VR) on each element of one triplet through metamorphic testing (MT), indicating the capacity to accommodate the diverse elements. Moreover, we propose an element-wise diversity-aware loss based on the VRs of aspect, opinion and sentiment, which can be jointly trained with existed ASTE models via uncertainty weighing. Conducted on four benchmark datasets and seven ASTE models, experimental results show that our MATO can enhance their diversity capacity, decreasing the average element-wise VRs by 3.28% to 15.36%. Meanwhile, our MATO is comparable to or better than those in terms of F1-score.

## introduction aspect sentiment triplet extraction (aste) aims to identify three elements, i.e., aspect term, opinion term and sentiment polarity as a triplet. as in the example "the sound is nice and loud; i do n't have any problems with hearing anything." in fig. 1 (c), its goal is to extract two triplets "(sound, nice, pos)" and "(sound, loud, pos)". many approaches to aste have been proposed successively. @xcite introduced the ## proposed method in this section, we present our mato in details as shown in fig. 2 . aste model can generate the hidden representations for aspect, opinion and sentiment through feature representation, feature learning and inference stages. aste model computes aste loss by comparing the labels. we use mt to indicate the capacity to diversity of aste model and introduce diversity-aware loss to enhance the perception ability. finally, an uncertainty weighing is applied to jointly train diversity-aware loss and aste loss. this process does not depend on the specific aste model (i.e., model-agnostic). ## experiments this paper aims to address three research questions. rq1: is the traditional reference-based validation method (i.g., f1-score) effective enough in revealing the diversity capacity of the aste model? rq3: does our mato significantly improve the capacity of the aste model when facing intertriplet and intra-triplet diverse expression? we conduct a thorough assessment of the quality of our designed mrs following @xcite , the results of which are shown in appendix a.3.