Chong Teng


2024

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Enhancing Cross-Document Event Coreference Resolution by Discourse Structure and Semantic Information
Qiang Gao | Bobo Li | Zixiang Meng | Yunlong Li | Jun Zhou | Fei Li | Chong Teng | Donghong Ji
Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)

Existing cross-document event coreference resolution models, which either compute mention similarity directly or enhance mention representation by extracting event arguments (such as location, time, agent, and patient), lackingmthe ability to utilize document-level information. As a result, they struggle to capture long-distance dependencies. This shortcoming leads to their underwhelming performance in determining coreference for the events where their argument information relies on long-distance dependencies. In light of these limitations, we propose the construction of document-level Rhetorical Structure Theory (RST) trees and cross-document Lexical Chains to model the structural and semantic information of documents. Subsequently, cross-document heterogeneous graphs are constructed and GAT is utilized to learn the representations of events. Finally, a pair scorer calculates the similarity between each pair of events and co-referred events can be recognized using standard clustering algorithm. Additionally, as the existing cross-document event coreference datasets are limited to English, we have developed a large-scale Chinese cross-document event coreference dataset to fill this gap, which comprises 53,066 event mentions and 4,476 clusters. After applying our model on the English and Chinese datasets respectively, it outperforms all baselines by large margins.

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What Factors Influence LLMs’ Judgments? A Case Study on Question Answering
Lei Chen | Bobo Li | Li Zheng | Haining Wang | Zixiang Meng | Runfeng Shi | Hao Fei | Jun Zhou | Fei Li | Chong Teng | Donghong Ji
Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)

Large Language Models (LLMs) are now being considered as judges of high efficiency to evaluate the quality of answers generated by candidate models. However, their judgments may be influenced by complex scenarios and inherent biases, raising concerns about their reliability. This study aims to bridge this gap by introducing four unexplored factors and examining the performance of LLMs as judges, namely answer quantity, inducing statements, judging strategy, and judging style. Additionally, we introduce a new dimension of question difficulty to provide a more comprehensive understanding of LLMs’ judgments across varying question intricacies. We employ ChatGPT, GPT-4, Gemini, and Claude-2 as judges and conduct experiments on Vicuna Benchmark and MT-bench. Our study reveals that LLMs’ judging abilities are susceptible to the influence of these four factors, and analyzing from the newly proposed dimension of question difficulty is highly necessary. We also provide valuable insights into optimizing LLMs’ performance as judges, enhancing their reliability and adaptability across diverse evaluation scenarios.

2014

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Word Sense Induction Using Lexical Chain based Hypergraph Model
Tao Qian | Donghong Ji | Mingyao Zhang | Chong Teng | Congling Xia
Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: Technical Papers

2012

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Context-Enhanced Personalized Social Summarization
Po Hu | Donghong Ji | Chong Teng | Yujing Guo
Proceedings of COLING 2012

2011

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Social Summarization via Automatically Discovered Social Context
Po Hu | Cheng Sun | Longfei Wu | Donghong Ji | Chong Teng
Proceedings of 5th International Joint Conference on Natural Language Processing

2009

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Query-Focused Multi-Document Summarization Using Co-Training Based Semi-Supervised Learning
Po Hu | Donghong Ji | Hai Wang | Chong Teng
Proceedings of the 23rd Pacific Asia Conference on Language, Information and Computation, Volume 1

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Finding Answers to Definition Questions Using Web Knowledge Bases
Han Ren | Donghong Ji | Jing Wan | Chong Teng
Proceedings of the 23rd Pacific Asia Conference on Language, Information and Computation, Volume 2