Papers with element-wise diversity-aware loss
MATO: A Model-Agnostic Training Optimization for Aspect Sentiment Triplet Extraction (2025.naacl-long)
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| Challenge: | Existing models with strong in-house performance may struggle to generalize to diverse expressions. |
| Approach: | They propose a model-agnostic t**raining method to improve ASTE model inference . they propose to compute the violation rate (VR) on each element of one triplet . |
| Outcome: | The proposed method can improve aspect sentiment triplet extraction models consistent with expected results facing triplet element diversity. |