Papers by Bingyang Ye

5 papers
Enhanced Noun-Noun Compound Interpretation through Textual Enrichment (2025.emnlp-main)

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Challenge: Recent benchmarks frame Noun-Noun Compound Interpretation as a multiple-choice question . but, it still faces key limitations: vague relation descriptions as options and inability to handle polysemous compounds.
Approach: They propose a textual enrichment framework that parses relations into eventoriented descriptions . the framework explicitly surfaces the hidden event connecting head and modifier .
Outcome: The proposed framework yields consistently higher accuracy across three LLM families.
Beyond Benchmarks: Building a Richer Cross-Document Event Coreference Dataset with Decontextualization (2025.naacl-long)

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Challenge: Existing datasets for Cross-Document Event Coreference (CDEC) are small and lacking diversity.
Approach: They propose a new approach leveraging large language models to decontextualize event mentions by simplifying the document-level annotation task to sentence pairs with enriched context.
Outcome: The proposed approach improves the quality of the dataset and generalizability of the model.
GLAMR: Augmenting AMR with GL-VerbNet Event Structure (2024.lrec-main)

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Challenge: Abstract Meaning Representation (AMR) is a general-purpose semantic encoding for language.
Approach: They propose an AMR interpretation of Generative Lexicon semantic components using a verb-net-encoded verb-node graph.
Outcome: The proposed extension is compatible with current AMR specification and can be automated.
Linguistically Conditioned Semantic Textual Similarity (2024.acl-long)

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Challenge: Semantic textual similarity (STS) is a fundamental NLP task that measures the semantic similarity between two sentences.
Approach: They propose to use a conditional STS dataset to measure sentences’ similarity conditioned on a certain aspect to reduce the inherent ambiguity posed by the sentences.
Outcome: The proposed method improves the performance over baselines on the C-STS dataset with over 80% F1 score.
The Coreference under Transformation Labeling Dataset: Entity Tracking in Procedural Texts Using Event Models (2023.findings-acl)

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Challenge: et al., 2023) show that entity coreference resolution is improved when events bring about changes in entities that are not reflected in text mentions.
Approach: They propose to perform transformation-based entity linking prior to coreference relation identification to improve entity coreference.
Outcome: The proposed model improves coreference resolution of entities mentioned under a process-oriented model of events.

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