Papers by Chee Seng Chan
An Embarrassingly Simple Approach for Intellectual Property Rights Protection on Recurrent Neural Networks (2022.aacl-main)
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| Challenge: | Existing protection schemes for deep neural network models protect intellectual property rights from being abused, stolen and plagiarized. |
| Approach: | They propose a practical approach for the IPR protection on recurrent neural networks without all the bells and whistles of existing IPR solutions. |
| Outcome: | The proposed approach is robust and effective against ambiguity and removal attacks on different RNN variants. |
Single-Pass, Depth-Selective Reading for Multi-Aspect Sentiment Analysis (2026.acl-long)
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| Challenge: | Existing models re-encode the sentence for each aspect or rely on static use of deep representations, leading to redundant computation and limited adaptivity. |
| Approach: | They propose a single-pass inference framework that encodes each sentence once to construct a reusable, depth-ordered substrate. |
| Outcome: | Experiments show that DABS reduces end-to-end computation by 60% in multi-aspect settings. |
MalayMMLU: A Multitask Benchmark for the Low-Resource Malay Language (2024.findings-emnlp)
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Soon Poh, Sze Jue Yang, Jeraelyn Tan, Lawrence Chieng, Jia Tan, Zhenyu Yu, Foong Mun, Chee Seng Chan
| Challenge: | Large Language Models (LLMs) and Large Vision Language Model (LVLMs) exhibit advanced proficiency in language reasoning and comprehension across a wide array of languages. |
| Approach: | They propose to use a multitask language understanding benchmark specifically designed for the Malay language to assess their proficiency. |
| Outcome: | The proposed model performs well in well-resourced languages, but in low-resource languages such as Bahasa Melayu, they are less studied due to a lack of studies and benchmarks. |
Banking Done Right: Redefining Retail Banking with Language-Centric AI (2025.emnlp-industry)
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Xin Jie Chua, Jeraelyn Ming Li Tan, Jia Xuan Tan, Soon Chang Poh, Yi Xian Goh, Debbie Hui Tian Choong, Foong Chee Mun, Sze Jue Yang, Chee Seng Chan
| Challenge: | This is the first global regulator-approved deployment where conversational AI functions as the primary banking interface. |
| Approach: | They propose a framework that powers a conversational AI framework that is powered by a closed-source LLM developed internally and replaces rigid multi-screen workflows with a single dialogue orchestrated by four LLM-powered agents. |
| Outcome: | The proposed framework replaces multi-screen workflows with a single dialogue orchestrated by four LLM-powered agents (Guardrails, Intent, Payment, and FAQ). |