Papers by Siyu Tang
Multi-turn Response Selection using Dialogue Dependency Relations (2020.emnlp-main)
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| Challenge: | Existing models for multi-turn response selection ignore the dependencies between the turns. |
| Approach: | They propose a dialogue extraction algorithm to transform a dialog history into threads based on their dependency relations. |
| Outcome: | The proposed model outperforms the state-of-the-art models on DSTC7 and DSTF8* with competitive results on UbuntuV2 . |
ContextBLIP: Doubly Contextual Alignment for Contrastive Image Retrieval from Linguistically Complex Descriptions (2024.findings-acl)
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Honglin Lin, Siyu Li, Guoshun Nan, Chaoyue Tang, Xueting Wang, Jingxin Xu, Rong Yankai, Zhouzhili Zhouzhili, Yutong Gao, Qimei Cui, Xiaofeng Tao
| Challenge: | Existing approaches to image retrieval from contextual descriptions (IRCD) lag behind human performance in IRCD. |
| Approach: | They propose a method that relies on a doubly contextual alignment scheme for challenging IRCD. |
| Outcome: | The proposed method can yield comparable results with GPT-4V, despite fewer parameters. |
Aspect-to-Scope Oriented Multi-view Contrastive Learning for Aspect-based Sentiment Analysis (2023.findings-emnlp)
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| Challenge: | Existing methods for Aspect-based sentiment analysis (ABSA) focus on mining syntactic or semantic information, which suffers from noisy interference when multiple aspects exist in a sentence. |
| Approach: | They propose a scope-assisted multi-view graph contrastive learning framework that captures correlation and difference between aspect and syntactic/semantic information. |
| Outcome: | The proposed framework outperforms state-of-the-art methods on five benchmark datasets and verifies its effectiveness and robustness. |
Scaling Down, Serving Fast: Compressing and Deploying Efficient LLMs for Recommendation Systems (2025.emnlp-industry)
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Kayhan Behdin, Ata Fatahibaarzi, Qingquan Song, Yun Dai, Aman Gupta, Zhipeng Wang, Hejian Sang, Shao Tang, Gregory Dexter, Sirou Zhu, Siyu Zhu, Tejas Dharamsi, Vignesh Kothapalli, Zhoutong Fu, Yihan Cao, Pin-Lun Hsu, Fedor Borisyuk, Natesh S. Pillai, Luke Simon, Rahul Mazumder
| Challenge: | Large language models (LLMs) have demonstrated remarkable performance across a wide range of industrial applications. |
| Approach: | They propose two techniques for training and deploying small language models that deliver high performance for a variety of industry use cases. |
| Outcome: | The proposed techniques retain much of the quality of larger models while reducing training/serving costs and latency. |
RepoShapley: Shapley-Enhanced Context Filtering for Repository-Level Code Completion (2026.findings-acl)
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| Challenge: | Large language models have strong reasoning, coding, and generation capabilities, but retrieval-augmented generation remains difficult under fixed context budgets. |
| Approach: | They propose a coalition-aware context filtering framework supervised by Shapley-style marginal contributions that captures sign effects via teacher-forced probing and computes exact Shaply values for small retrieval sets. |
| Outcome: | Experiments show that RepoShapley improves completion quality while reducing harmful context and unnecessary retrieval. |
Affective Knowledge Enhanced Multiple-Graph Fusion Networks for Aspect-based Sentiment Analysis (2022.emnlp-main)
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| Challenge: | Existing methods for sentiment analysis ignore the roles of syntax dependency relation labels and affective semantic information in determining the sentiment polarity of social media users. |
| Approach: | They propose a new multi-graph fusion network to leverage the richer syntax dependency relation labels and affective semantic information of words. |
| Outcome: | The proposed model outperforms state-of-the-art methods on three datasets. |
Improving Multi-task Stance Detection with Multi-task Interaction Network (2022.emnlp-main)
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| Challenge: | Recent studies have proposed multi-task learning models that introduce sentiment information to boost stance detection but neglect to capture the fine-grained task-specific interaction between stance and sentiment tasks, thus degrading performance. |
| Approach: | They propose a novel multi-task interaction network (MTIN) that captures the word-level interaction between tasks, so as to obtain richer task representations. |
| Outcome: | The proposed approach outperforms state-of-the-art methods on two real-world datasets. |
Human-Agent Collaborative Paper-to-Page Crafting (2026.findings-acl)
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Qianli Ma, Siyu Wang, Chen Yilin, Yinhao Tang, Yixiang Yang, Chang Guo, Bingjie Gao, Zhening Xing, Yanan Sun, Zhipeng Zhang
| Challenge: | Existing approaches to create project pages from academic papers have focused on static slides and posters, but the dynamic nature of webpages remains an unaddressed challenge. |
| Approach: | They propose a novel multi-agent system that deconstructs paper-to-page creation into a coarse-to fine pipeline from narrative planning to multimodal content generation and interactive rendering. |
| Outcome: | The proposed system generates high-quality, visually appealing pages in under 15 minutes for less than $0.1 . |