Papers by Ying-Jia Lin
Improving Multi-Criteria Chinese Word Segmentation through Learning Sentence Representation (2023.findings-emnlp)
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| Challenge: | Recent Chinese word segmentation models tend to learn the segmentation knowledge through in-vocabulary words rather than understanding the meaning of the entire context. |
| Approach: | They propose a context-aware approach that incorporates unsupervised sentence representation learning over different dropout masks into the multi-criteria training framework. |
| Outcome: | The proposed approach achieves state-of-the-art (SoTA) performance on six of the nine CWS benchmark datasets and out-of vocabulary (OOV) recalls for eight of nine. |
Improved Unsupervised Chinese Word Segmentation Using Pre-trained Knowledge and Pseudo-labeling Transfer (2023.emnlp-main)
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| Challenge: | Existing approaches to unsupervised Chinese word segmentation require multiple inferences to perform word segmenting. |
| Approach: | They propose a method that integrates the segmentation signal from an unsupervised language model to a pre-trained BERT classifier under a pseudo-labeling framework. |
| Outcome: | The proposed method achieves state-of-the-art performance on the eight UCWS tasks while significantly reducing training time compared to previous approaches. |
Unsupervised Single Document Abstractive Summarization using Semantic Units (2022.aacl-main)
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| Challenge: | a lack of sufficient training pairs is a common issue in real-world applications. |
| Approach: | They propose a framework that lets a model learn the frequency of each semantic unit in the source text. |
| Outcome: | The proposed model outperforms other unsupervised methods under CNN/Daily Mail task. |
MAPLE: Enhancing Review Generation with Multi-Aspect Prompt LEarning in Explainable Recommendation (2025.acl-long)
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Ching-Wen Yang, Zhi-Quan Feng, Ying-Jia Lin, Che Wei Chen, Kun-da Wu, Hao Xu, Yao Jui-Feng, Hung-Yu Kao
| Challenge: | Existing models that generate generic aspects do not provide personalized informative recommendations. |
| Approach: | They propose a model that integrates aspect category as another input dimension to facilitate memorizing fine-grained aspect terms. |
| Outcome: | The proposed model outperforms baseline model on restaurant review datasets in the restaurant domain. |
SCURank: Ranking Multiple Candidate Summaries with Summary Content Units for Enhanced Summarization (2026.findings-acl)
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| Challenge: | Existing ranking strategies for large language models suffer from instability and lack of information content. |
| Approach: | They propose a framework that enhances summarization by leveraging Summary Content Units (SCUs) they investigate the effectiveness of SCURank in distilling summaries from multiple LLMs . |
| Outcome: | The proposed framework outperforms traditional metrics and LLM-based ranking methods in summarization tasks. |