Papers by Lay-Ki Soon
Can ChatGPT Perform Reasoning Using the IRAC Method in Analyzing Legal Scenarios Like a Lawyer? (2023.findings-emnlp)
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| Challenge: | Large Language Models (LLMs) have attracted a lot of attention in the legal domain due to their ability to tackle a variety of legal tasks. |
| Approach: | They constructed a corpus consisting of two legal scenarios using the IRAC method and used it to perform analysis on the corpus. |
| Outcome: | The proposed model can analyze a contract act in Malaysia and the Australian Social Act for Dependent Child using the IRAC method. |
CrudeOilNews: An Annotated Crude Oil News Corpus for Event Extraction (2022.lrec-1)
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| Challenge: | a corpus of English crude oil news for event extraction is presented . the corpus contains 425 news articles with approximately 11k events annotated . |
| Approach: | They present a corpus of English Crude Oil news for event extraction . it is the first of its kind for Commodity News and contributes to text mining . |
| Outcome: | The proposed corpus of English crude oil news is the first of its kind for Commodity News . the annotated news articles are compared with the standard news articles . |
Malaysian English News Decoded: A Linguistic Resource for Named Entity and Relation Extraction (2024.lrec-main)
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| Challenge: | Standard English and Malaysian English exhibit significant differences in morphosyntactic variations . existing datasets are not sufficient to enhance NLP tasks in Malaysian english . |
| Approach: | They propose to use a Malaysian English news article dataset to refine NER models for Malaysian english. |
| Outcome: | The proposed dataset can improve the performance of NER on Malaysian English. |
RENOVI: A Benchmark Towards Remediating Norm Violations in Socio-Cultural Conversations (2024.findings-naacl)
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Haolan Zhan, Zhuang Li, Xiaoxi Kang, Tao Feng, Yuncheng Hua, Lizhen Qu, Yi Ying, Mei Rianto Chandra, Kelly Rosalin, Jureynolds Jureynolds, Suraj Sharma, Shilin Qu, Linhao Luo, Ingrid Zukerman, Lay-Ki Soon, Zhaleh Semnani Azad, Reza Haf
| Challenge: | Norm violations occur when individuals fail to conform to culturally accepted behaviors, which may lead to potential conflicts. |
| Approach: | They propose to use a large corpus of 9,258 multi-turn dialogues annotated with social norms to equip AI systems with a remediation ability. |
| Outcome: | The proposed system can understand and remediate norm violations step by step. |
Hybrid Models for Aspects Extraction without Labelled Dataset (D19-66)
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| Challenge: | Existing methods to extract aspects from opinions focus on explicit aspects, but sentences do not state them explicitly. |
| Approach: | They propose to use a dictionary-based approach to identify and extract aspects from opinions . they propose to combine topic modelling and dictionary--based method . |
| Outcome: | The proposed models outperform baseline topic model and dictionary-based approach in 58.70% of the evaluations. |
Document-Level Zero-Shot Relation Extraction with Entity Side Information (2026.eacl-long)
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| Challenge: | Existing approaches rely on Large Language Models (LLMs) to generate synthetic data for unseen labels. |
| Approach: | They propose a document-level zero-shot relation extraction framework with Entity Side Information to solve existing problems. |
| Outcome: | The proposed approach achieves an average improvement of 11.6% in the macro F1-Score compared to baseline models and existing benchmarks. |
ACCESS : A Benchmark for Abstract Causal Event Discovery and Reasoning (2025.naacl-long)
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Vy Vo, Lizhen Qu, Tao Feng, Yuncheng Hua, Xiaoxi Kang, Songhai Fan, Tim Dwyer, Lay-Ki Soon, Gholamreza Haffari
| Challenge: | Existing methods for identifying event causality in NLP are limited in their scale and rely on lexical cues. |
| Approach: | They propose a benchmark for identifying abstract causality from a large-scale dataset. |
| Outcome: | The proposed benchmark can be leveraged for enhancing QA reasoning performance in LLMs. |
LePREC: Reasoning as Classification over Structured Factors for Assessing Relevance of Legal Issues (2026.acl-long)
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Fanyu Wang, Xiaoxi Kang, Paul Burgess, Aashish Srivastava, Chetan Arora, Adnan Trakic, Lay-Ki Soon, Md Khalid Hossain, Lizhen Qu
| Challenge: | Large language models (LLMs) have impressive reasoning capabilities, but their precision remains inadequate. |
| Approach: | They propose a framework that integrates neural generation with statistical reasoning to improve the accuracy of large language models. |
| Outcome: | The proposed framework achieves interpretability through transparent feature weighting while maintaining data efficiency through correlation-based statistical classification. |
Who You Are, What You Say: Intra- and Inter- Context Personality for Emotion Recognition in Conversation (2026.findings-eacl)
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| Challenge: | Existing approaches to Emotion Recognition in conversation (ERC) focus on modeling speaker dynamics within dialogues. |
| Approach: | They propose a personality-aware ERC framework that segregates conversational context into intra- and inter-speaker components and models static or dynamic personality traits to represent stable and evolving speaker dispositions. |
| Outcome: | The proposed framework improves weighted F1 by 2.74% over non-LLM methods and 0.98% over recent LLM-based methods. |