Papers by Chenhao Xie
Revisiting the Negative Data of Distantly Supervised Relation Extraction (2021.acl-long)
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| Challenge: | Existing methods for relation extraction with distant supervision generate plenty of training samples but noisy labels and imbalanced training data cause problems. |
| Approach: | They propose a method that automatically labels a sentence with relational triples from a knowledge base. |
| Outcome: | The proposed method outperforms existing methods even with false positive samples. |
MoVa: Towards Generalizable Classification of Human Morals and Values (2025.emnlp-main)
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Ziyu Chen, Junfei Sun, Chenxi Li, Tuan Dung Nguyen, Jing Yao, Xiaoyuan Yi, Xing Xie, Chenhao Tan, Lexing Xie
| Challenge: | Identifying human morals and values embedded in language is essential to empirical studies of communication. |
| Approach: | They propose a framework for generalizable classification of human morals and values . they recommend a classification strategy that scores all related concepts simultaneously . |
| Outcome: | The proposed method outperforms fine-tuned models across domains and frameworks. |
Parsing Natural Language into Propositional and First-Order Logic with Dual Reinforcement Learning (2022.coling-1)
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Xuantao Lu, Jingping Liu, Zhouhong Gu, Hanwen Tong, Chenhao Xie, Junyang Huang, Yanghua Xiao, Wenguang Wang
| Challenge: | Existing methods to parse natural language into structured logical expressions have limitations due to paucity of labeled data. |
| Approach: | They propose a scoring model to automatically learn a model-based reward . they also propose introducing a Chinese-PL/FOL dataset to compensate for paucity of labeled data . |
| Outcome: | The proposed model outperforms competitors on several datasets. |
Gen-SQL: Efficient Text-to-SQL By Bridging Natural Language Question And Database Schema With Pseudo-Schema (2025.coling-main)
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| Challenge: | Recent studies have shifted paradigms and leveraged Large Language Models (LLMs) to tackle the challenging task of Text-to-SQL. |
| Approach: | They propose a framework that leverages large language models to generate SQL queries . they exploit prior knowledge from the LLM to enhance embedding-based retriever . |
| Outcome: | The proposed method improves embedding-based retriever and reduces cost. |