Papers with metamorphic testing

2 papers
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.
SALTED: A Framework for SAlient Long-tail Translation Error Detection (2022.findings-emnlp)

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Challenge: Traditional machine translation metrics are insensitive to the long tail of behavioral problems.
Approach: They propose a specification-based framework for behavioral testing of NMT models . they use high-precision detectors that flag errors between a source sentence and output .
Outcome: The proposed framework provides a reliable view of problems that were previously invisible.

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