Papers by Shengfei Lyu

3 papers
Generation-Augmented and Embedding Fusion in Document-Level Event Argument Extraction (2025.coling-main)

Copied to clipboard

Challenge: Document-level event argument extraction is a crucial task that aims to extract arguments from the entire document, beyond sentence-level analysis.
Approach: They propose a novel approach to document-level event argument extraction that integrates predefined templates and generative language models into a foundational embedding derived from a classification model.
Outcome: The proposed approach is more effective than baseline models and data-efficient in low-resource scenarios.
ESGenius: Benchmarking LLMs on Environmental, Social, and Governance (ESG) and Sustainability Knowledge (2025.emnlp-main)

Copied to clipboard

Challenge: ESGenius is a comprehensive benchmark for evaluating Large Language Models on ESG and sustainability knowledge.
Approach: They introduce ESGenius, a benchmark for evaluating and enhancing ESG proficiency . they use a rigorous two-stage evaluation protocol and a repository of foundational frameworks .
Outcome: ESGenius is a benchmark for evaluating and enhancing the proficiency of Large Language Models (LLMs) in ESG and sustainability-focused question answering.
Relation Classification with Entity Type Restriction (2021.findings-acl)

Copied to clipboard

Challenge: Existing methods regard all relations as candidate relations for the two entities, which leads to inappropriate relations being candidate relations.
Approach: They propose a paradigm which exploits entity types to restrict candidate relations by mutual restrictions.
Outcome: The proposed paradigm improves GCN and SpanBERT on a standard dataset by 6.9 and 4.4 F1 points.

What is GenGO?

GenGO is an NLP powered publication search system. It currenctly indexes 30k+ papers from ACL Anthology, and implements multi-aspect summarization, semantic search, and more!

Information

About
Limitations