Papers by Nicholas Jing Yuan
An In-depth Study on Internal Structure of Chinese Words (2021.acl-long)
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Chen Gong, Saihao Huang, Houquan Zhou, Zhenghua Li, Min Zhang, Zhefeng Wang, Baoxing Huai, Nicholas Jing Yuan
| Challenge: | Unlike English letters, Chinese characters have rich and specific meanings. |
| Approach: | They propose to model Chinese words' internal structures as dependency trees with 11 labels for distinguishing syntactic relationships. |
| Outcome: | The proposed model of Chinese word-internal structures shows it can be used to parse sentences . it shows that the model can be applied to a sentence-level task with a competitive dependency parser. |
HacRED: A Large-Scale Relation Extraction Dataset Toward Hard Cases in Practical Applications (2021.findings-acl)
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Qiao Cheng, Juntao Liu, Xiaoye Qu, Jin Zhao, Jiaqing Liang, Zhefeng Wang, Baoxing Huai, Nicholas Jing Yuan, Yanghua Xiao
| Challenge: | Relation extraction (RE) is an essential topic in natural language processing and has attracted extensive attention. |
| Approach: | They propose a case-oriented construction framework to build a hard case relation extraction dataset with 65,225 relational facts annotated from 9,231 documents. |
| Outcome: | The proposed model achieves a high 96% F1 score on data quality and is far lower than humans. |
A Rigorous Study on Named Entity Recognition: Can Fine-tuning Pretrained Model Lead to the Promised Land? (2020.emnlp-main)
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| Challenge: | Named entity recognition (NER) is a fundamental task of information extraction. |
| Approach: | They propose to perform randomization tests on standard NER benchmarks to examine name regularity, mention coverage and context diversity. |
| Outcome: | The proposed model performs better on standard NER benchmarks than other models on open datasets. |
Delving Deep into Regularity: A Simple but Effective Method for Chinese Named Entity Recognition (2022.findings-naacl)
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| Challenge: | Named entity recognition (NER) is a system for identifying text spans pertaining to specific entity types. |
| Approach: | They propose a method to investigate the regularity of Chinese NER's entity mentions by a regularity-aware module and a periodicity-gnostic module. |
| Outcome: | The proposed model significantly outperforms previous state-of-the-art methods on three benchmark datasets and a practical medical dataset. |
BERT-MK: Integrating Graph Contextualized Knowledge into Pre-trained Language Models (2020.findings-emnlp)
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| Challenge: | Existing knowledge representation learning methods do not use graph contextualized knowledge. |
| Approach: | They propose to model subgraphs in a medical KG and integrate it with a pre-trained language model to do knowledge generalization. |
| Outcome: | The proposed model achieves state-of-the-art on several medical NLP tasks . it improves on MedERNIE, and the proposed model is effective . |
APGN: Adversarial and Parameter Generation Networks for Multi-Source Cross-Domain Dependency Parsing (2021.findings-emnlp)
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| Challenge: | Existing models for dependency parsing use labeled training data for several fixed domains, but performance drops when labeles only exist for several out-domains. |
| Approach: | They propose a model for multi-source cross-domain dependency parsing that uses a parameter generation network and adversarial network for learning domain-invariant representations. |
| Outcome: | The proposed model improves cross-domain parsing performance by about 2 points over strong BERT-enhanced baselines over a recently released dataset for multi-domain dependency parse. |
Few-shot Knowledge Graph-to-Text Generation with Pretrained Language Models (2021.findings-acl)
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| Challenge: | Existing models for KG-to-text generation are based on pretrained language models. |
| Approach: | They propose to automatically generate a text that describes the facts in knowledge graph (KG) they leverage the excellent capacities of pretrained language models (PLMs) in language understanding and generation. |
| Outcome: | The proposed model outperforms all comparison methods on fully-supervised and fewshot settings. |
Explaining Length Bias in LLM-Based Preference Evaluations (2025.findings-emnlp)
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Zhengyu Hu, Linxin Song, Jieyu Zhang, Zheyuan Xiao, Tianfu Wang, Zhengyu Chen, Nicholas Jing Yuan, Jianxun Lian, Kaize Ding, Hui Xiong
| Challenge: | a preference evaluation metric is often biased towards longer responses, revealing a reliability problem . a decomposition of the preference evaluation into two components is needed to understand this bias. |
| Approach: | They propose to decompose the preference evaluation metric into two key components . the first component is length-dependent and related to trustworthiness . |
| Outcome: | The proposed evaluation metric is based on two components: desirability and information mass. |