Papers by Jinxia Lin

2 papers
Low-Hallucination and Efficient Coreference Resolution with LLMs (2025.findings-emnlp)

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Challenge: Large Language Models have shown promising results in coreference resolution, but they face a critical issue: hallucinations.
Approach: They propose a low-hallucination and efficient solution to the problem of hallucinations . they propose efficient constrained decoding for coreference resolution .
Outcome: The proposed approach achieves better performance on the English OntoNotes development set.
Enhancing Metaphor Detection by Gloss-based Interpretations (2021.findings-acl)

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Challenge: Existing approaches to metaphor detection are limited by ambiguous meanings of metaphorical substitute words.
Approach: They propose a model that utilizes glosses to interpret metaphorical words by enhancing three datasets with gloss annotations.
Outcome: The proposed model outperforms state-of-the-art models on three enhanced datasets and that gloss-based interpretation benefits metaphor detection.

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