Shuqi Sun


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An Exploratory Study on Model Compression for Text-to-SQL
Shuo Sun | Yuze Gao | Yuchen Zhang | Jian Su | Bin Chen | Yingzhan Lin | Shuqi Sun
Findings of the Association for Computational Linguistics: ACL 2023

Text-to-SQL translates user queries into SQL statements that can retrieve relevant answers from relational databases. Recent approaches to Text-to-SQL rely on pre-trained language models that are computationally expensive and technically challenging to deploy in real-world applications that require real-time or on-device processing capabilities. In this paper, we perform a focused study on the feasibility of applying recent model compression techniques to sketch-based and sequence-to-sequence Text-to-SQL models. Our results reveal that sketch-based Text-to-SQL models generally have higher inference efficiency and respond better to model compression than sequence-to-sequence models, making them ideal for real-world deployments, especially in use cases with simple SQL statements.


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Q-TOD: A Query-driven Task-oriented Dialogue System
Xin Tian | Yingzhan Lin | Mengfei Song | Siqi Bao | Fan Wang | Huang He | Shuqi Sun | Hua Wu
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

Existing pipelined task-oriented dialogue systems usually have difficulties adapting to unseen domains, whereas end-to-end systems are plagued by large-scale knowledge bases in practice. In this paper, we introduce a novel query-driven task-oriented dialogue system, namely Q-TOD. The essential information from the dialogue context is extracted into a query, which is further employed to retrieve relevant knowledge records for response generation. Firstly, as the query is in the form of natural language and not confined to the schema of the knowledge base, the issue of domain adaption is alleviated remarkably in Q-TOD. Secondly, as the query enables the decoupling of knowledge retrieval from the generation, Q-TOD gets rid of the issue of knowledge base scalability. To evaluate the effectiveness of the proposed Q-TOD, we collect query annotations for three publicly available task-oriented dialogue datasets. Comprehensive experiments verify that Q-TOD outperforms strong baselines and establishes a new state-of-the-art performance on these datasets.


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Amendable Generation for Dialogue State Tracking
Xin Tian | Liankai Huang | Yingzhan Lin | Siqi Bao | Huang He | Yunyi Yang | Hua Wu | Fan Wang | Shuqi Sun
Proceedings of the 3rd Workshop on Natural Language Processing for Conversational AI

In task-oriented dialogue systems, recent dialogue state tracking methods tend to perform one-pass generation of the dialogue state based on the previous dialogue state. The mistakes of these models made at the current turn are prone to be carried over to the next turn, causing error propagation. In this paper, we propose a novel Amendable Generation for Dialogue State Tracking (AG-DST), which contains a two-pass generation process: (1) generating a primitive dialogue state based on the dialogue of the current turn and the previous dialogue state, and (2) amending the primitive dialogue state from the first pass. With the additional amending generation pass, our model is tasked to learn more robust dialogue state tracking by amending the errors that still exist in the primitive dialogue state, which plays the role of reviser in the double-checking process and alleviates unnecessary error propagation. Experimental results show that AG-DST significantly outperforms previous works in two active DST datasets (MultiWOZ 2.2 and WOZ 2.0), achieving new state-of-the-art performances.


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A Hierarchical Semantics-Aware Distributional Similarity Scheme
Shuqi Sun | Ke Sun | Shiqi Zhao | Haifeng Wang | Muyun Yang | Sheng Li
Proceedings of the Sixth International Joint Conference on Natural Language Processing


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Harvesting Related Entities with a Search Engine
Shuqi Sun | Shiqi Zhao | Muyun Yang | Haifeng Wang | Sheng Li
Proceedings of 5th International Joint Conference on Natural Language Processing

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Nonlinear Evidence Fusion and Propagation for Hyponymy Relation Mining
Fan Zhang | Shuming Shi | Jing Liu | Shuqi Sun | Chin-Yew Lin
Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies


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Utilizing Variability of Time and Term Content, within and across Users in Session Detection
Shuqi Sun | Sheng Li | Muyun Yang | Haoliang Qi | Tiejun Zhao
Coling 2010: Posters


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A Re-examination on Features in Regression Based Approach to Automatic MT Evaluation
Shuqi Sun | Yin Chen | Jufeng Li
Proceedings of the ACL-08: HLT Student Research Workshop