Papers by Sophia Yat Mei Lee
A Comprehensive Graph Framework for Question Answering with Mode-Seeking Preference Alignment (2025.findings-acl)
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| Challenge: | Existing studies struggle with achieving global understanding of large language models . GraphMPA is a graph-based framework with mode-seeking preference alignment . |
| Approach: | They propose a graph-based framework with mode-seeking preference alignment to improve model outputs. |
| Outcome: | The proposed framework constructs a hierarchical document graph mimicking human cognitive processes for information understanding and synthesis. |
Zero-shot Cross-lingual NER via Mitigating Language Difference: An Entity-aligned Translation Perspective (2025.findings-emnlp)
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| Challenge: | Existing approaches to cross-lingual Named Entity Recognition focus on Latin script language (LSL) for non-Latin script language, performance often degrades due to deep structural differences. |
| Approach: | They propose an entity-aligned translation approach to align entities between NSL and English . |
| Outcome: | The proposed approach aims to transfer knowledge from high-resource languages to low-resourced languages. |
One-Teacher and Multiple-Student Knowledge Distillation on Sentiment Classification (2022.coling-1)
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| Challenge: | Existing knowledge distillation models require large computing resources and long inference time to perform. |
| Approach: | They propose a one-teacher and multiple-student knowledge distillation approach to distill a deep pre-trained teacher model into multiple shallow student models with ensemble learning. |
| Outcome: | The proposed method achieves better results with fewer parameters and extremely high speedup ratios on three sentiment classification tasks. |
An Event-comment Social Media Corpus for Implicit Emotion Analysis (2020.lrec-1)
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| Challenge: | Existing methods for identifying implicit emotions have been poor in analyzing explicit emotions. |
| Approach: | They propose to construct a Chinese eventcomment social media emotion corpus which deals with both explicit and implicit emotions with more emphasis being placed on the implicit ones. |
| Outcome: | The proposed corpus will be useful for both explicit and implicit emotion classification and detection as well as event classification. |