Papers by Shuai Pang
Mention Extraction and Linking for SQL Query Generation (2020.emnlp-main)
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| Challenge: | Existing text-to-SQL systems take a slot-filling approach, but they are limited in capturing inter-dependencies among SQL clauses. |
| Approach: | They propose an extraction-linking approach where a unified extractor recognizes all types of slot mentions appearing in the question sentence before a linker maps the recognized columns to the table schema to generate executable SQL queries. |
| Outcome: | The proposed method achieves the first place on the WikiSQL benchmark. |
DREAM: Improving Video-Text Retrieval Through Relevance-Based Augmentation Using Large Foundation Models (2025.naacl-long)
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| Challenge: | Recent advances in video-text retrieval models have limited training data annotations. |
| Approach: | They propose a Video-Text Retrieval Paradigm with Relevance-based Augmentation which enhances video and text data using large foundation models to learn more generalized features. |
| Outcome: | The proposed method improves video-text retrieval performance over existing methods. |
Breaking the Evaluation Paradox: Evaluating High-Entropy Search with Computationally Irreducible Constraints (2026.findings-acl)
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| Challenge: | a new framework for evaluation of exhaustive search capabilities is needed . high-entropy enumeration tasks make such ground truth impossible for humans to create . VERITAS is a framework built on the principle of computationally irreducible constraints . |
| Approach: | They propose a framework that uses non-optimizable constraints to create verifiable searches . VERITAS can generate infinite number of test cases with perfect ground truth and precise difficulty control . |
| Outcome: | a new evaluation framework for large language models is based on non-optimizable constraints . the framework can generate infinite number of test cases with perfect ground truth and precise difficulty control . |
FASTMATCH: Accelerating the Inference of BERT-based Text Matching (2020.coling-main)
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| Challenge: | Recent pre-trained language models have shown state-of-the-art accuracies in text matching. |
| Approach: | They propose a BERT-based text matching model where representations and interactions are decoupled . they propose generating final matching scores using a lightweight attention network . |
| Outcome: | Experiments show that the proposed model can achieve up to 100X speed-up to BERT and RoBERTa while keeping more up to 98.7% of the performance. |