Papers by Ting Lin
Lightweight Spatial Modeling for Combinatorial Information Extraction From Documents (2023.findings-eacl)
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Yanfei Dong, Lambert Deng, Jiazheng Zhang, Xiaodong Yu, Ting Lin, Francesco Gelli, Soujanya Poria, Wee Sun Lee
| Challenge: | Existing datasets do not cover documents with complex spatial structures and a lack of spatial information for document entity classification. |
| Approach: | They propose a new spatial bias in attention calculation based on the K-nearest-neighbor graph of document entities that limits entities’ attention to their local radius. |
| Outcome: | The proposed model outperforms baselines in most entity types and is highly parameter-efficient compared to existing methods. |
TACLR: A Scalable and Efficient Retrieval-based Method for Industrial Product Attribute Value Identification (2025.acl-long)
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Yindu Su, Huike Zou, Lin Sun, Ting Zhang, Haiyang Yang, Chen Li Yu, David Lo, Qingheng Zhang, Shuguang Han, Jufeng Chen
| Challenge: | Existing methods for product attribute value identification face critical challenges . seller-provided attribute values are often incomplete or inaccurate . |
| Approach: | They propose a retrieval-based method that uses taxonomy-aware contrastive learning . they use product profiles and candidate values to encode and retrieve attributes based on similarity . |
| Outcome: | The proposed method is based on a taxonomy-aware, hard negative sampling and adaptive inference with dynamic thresholds. |
Enhancing Dialogue Symptom Diagnosis with Global Attention and Symptom Graph (D19-1)
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| Challenge: | Existing studies on symptom diagnosis based on EHRs focus on the standard electronic medical records, but the dialogues between doctors and patients that contain more rich information are not well studied. |
| Approach: | They propose to build a global attention mechanism to capture more symptom related information and build symptom graphs to model the associations between symptoms rather than treating each symptom independently. |
| Outcome: | The proposed model achieves the state-of-the-art on the constructed dataset. |
ORANGE: Text-video Retrieval via Watch-time-aware Heterogeneous Graph Contrastive Learning (2023.emnlp-industry)
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Yucheng Lin, Tim Chang, Yaning Chang, Jianqiang Ma, Donghui Li, Ting Peng, Zang Li, Zhiyi Zhou, Feng Wang
| Challenge: | Existing methods for text-video retrieval focus on informative representations and delicate matching mechanisms, but real-world scenarios often involve brief, ambiguous queries and low-quality videos. |
| Approach: | They propose a novel method to learn informative embeddings for queries and videos . they use a watch-time-aware contrastive learning paradigm to capture dependencies . |
| Outcome: | The proposed method is effective in a real-world video-search service. |
Beyond Uniform SVD: Dual-Level Optimization across Columns and Modules for LLM Compression (2026.findings-acl)
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| Challenge: | Existing methods for low-rank decomposition overlook decomposing errors and suboptimal approximation. |
| Approach: | They propose a low-rank decomposition framework that integrates low-level optimization at column and module levels. |
| Outcome: | The proposed framework outperforms state-of-the-art methods and baselines in SVD and pruning. |
DisCo: Distilled Student Models Co-training for Semi-supervised Text Mining (2023.emnlp-main)
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| Challenge: | Existing text mining models are fine-tuned by fine-timing a large pre-trained language model (PLM) in downstream tasks. |
| Approach: | They propose a semi-supervised learning framework for fine-tuning a cohort of small student models generated from a large pre-trained language model using knowledge distillation. |
| Outcome: | The proposed framework outperforms baseline models on semi-supervised text classification and extractive summarization tasks while maintaining comparable performance. |