Papers by Xiaoye Ouyang

4 papers
Chinese Lexical Substitution: Dataset and Method (2023.emnlp-main)

Copied to clipboard

Challenge: Existing benchmarks for lexical substitution (LS) are limited and limited in coverage . despite extensive research on Lexical Substitution in various languages, there is limited evidence for LS in Chinese.
Approach: They propose to use human and machine collaboration to construct a Chinese LS dataset . they combine four unsupervised LS methods to generate candidate substitutes .
Outcome: The proposed method outperforms existing benchmarks on the Chinese lexical substitution task.
Chinese Morph Resolution in E-commerce Live Streaming Scenarios (2025.naacl-industry)

Copied to clipboard

Challenge: Live morph resolution task is used to detect e-commerce live streaming violations . morphs are used to evade scrutiny and engage in false advertising .
Approach: They propose a task to detect morph violations in live streaming scenarios . they use large language models to generate additional training data .
Outcome: The proposed method improves performance and improves live streaming regulation.
Collaborative Document Simplification Using Multi-Agent Systems (2025.coling-main)

Copied to clipboard

Challenge: Document simplification requires complex factors such as technical terminology, metaphors, and overall coherence.
Approach: They propose a multi-agent framework for document simplification based on large language models that emulates the collaborative process of a human expert team through the roles played by multiple agents.
Outcome: The proposed framework emulates the collaborative process of a human expert team through the roles played by multiple agents, addressing the intricate demands of document simplification.
Post-Hoc Watermarking for Robust Detection in Text Generated by Large Language Models (2025.coling-main)

Copied to clipboard

Challenge: Existing methods for document simplification address complex factors such as technical terminology, metaphors, and overall coherence.
Approach: They propose a multi-agent framework AgentSimp for document simplification based on large language models that simulates collaboration among agents through roles played by multiple agents.
Outcome: The proposed framework produces simplified documents that are more thoroughly simplified and more coherent across various articles and styles.

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