Papers by Yitao Cai
IGSQL: Database Schema Interaction Graph Based Neural Model for Context-Dependent Text-to-SQL Generation (2020.emnlp-main)
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| Challenge: | Existing models on context-dependent text-to-SQL task focus on utilizing historic user inputs. |
| Approach: | They propose a database schema interaction graph encoder to utilize historic user inputs. |
| Outcome: | The proposed model outperforms previous state-of-the-art models on two datasets . it also outperformed existing models on the benchmark SParC and CoSQL datasets. |
Revisiting Pivot-Based Paraphrase Generation: Language Is Not the Only Optional Pivot (2021.emnlp-main)
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| Challenge: | Existing methods for paraphrase generation rely on language as the pivot . however, there is no evidence that parallel data of paraphrases is needed for paraphrasing. |
| Approach: | They propose to use semantic and syntactic representations as pivot for paraphrase generation. |
| Outcome: | The proposed method can generate paraphrases with better quality than using language as pivot. |
Multi-Modal Sarcasm Detection in Twitter with Hierarchical Fusion Model (P19-1)
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| Challenge: | Existing methods to detect sarcasm focus on text, but they are insufficient for multi-modal messages. |
| Approach: | They propose a multi-modal hierarchical sarcasm detection model for tweets consisting of texts and images in Twitter. |
| Outcome: | The proposed model is able to detect sarcasm on twitter using three modalities . the proposed model can be used in customer service, opinion mining and harassment detection . |
Counteracting the Matthew Effect in Self-Improvement of LVLMs through Head-Tail Re-balancing (2026.acl-long)
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Xin Guo, Zhiheng Xi, Yiwen Ding, Yitao Zhai, Xiaowei Shi, Xunliang Cai, Tao Gui, Qi Zhang, Xuanjing Huang
| Challenge: | Large vision language models have impressive reasoning capabilities across complex multimodal tasks. |
| Approach: | They propose to use distribution-reshaping and trajectory-rebalancing to improve visual reasoning capabilities. |
| Outcome: | Experiments on Qwen2-VL-7B-Instruct and InternVL2.5-4B models show that their methods outperform baselines by 3.86 points. |
Mitigating Tail Narrowing in LLM Self-Improvement via Socratic-Guided Sampling (2025.naacl-long)
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Yiwen Ding, Zhiheng Xi, Wei He, Lizhuoyuan Lizhuoyuan, Yitao Zhai, Shi Xiaowei, Xunliang Cai, Tao Gui, Qi Zhang, Xuanjing Huang
| Challenge: | Large language models (LLMs) generate solutions themselves and iteratively train on filtered, high-quality rationales, but performance reaches a ceiling after a few iterations. |
| Approach: | They propose a strategy to improve the efficiency of sampling heavy-tailed data by using Socratic-style guidance signals to help LLMs reasoning with complex queries. |
| Outcome: | The proposed approach is effective on difficult queries and on held-out tasks, while requiring human supervision. |
Sense-Aware Neural Models for Pun Location in Texts (P18-2)
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| Challenge: | Puns where the two meanings share the same pronunciation are known as homographic puns. |
| Approach: | They propose a sense-aware neural model to address the task of pun location . they first obtain several WSD results for the text and then leverage a bidirectional LSTM network to model each word senses. |
| Outcome: | The proposed model is based on a SemEval 2017 benchmark dataset showing that it can predict homographic puns. |
Making Better Use of Bilingual Information for Cross-Lingual AMR Parsing (2021.findings-acl)
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| Challenge: | Existing work on meaning representations for English and other languages finds that concepts in their predicted AMR graphs are less specific. |
| Approach: | They propose a cross-lingual AMR parser that can predict more precise concepts by translating translated texts and non-English texts. |
| Outcome: | The proposed model surpasses state-of-the-art parser by 10.6 points on Smatch F1 score. |
Towards Document-Level Paraphrase Generation with Sentence Rewriting and Reordering (2021.findings-emnlp)
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| Challenge: | Document-level paraphrase generation is an important task in natural language processing. |
| Approach: | They propose a coherence relationship-guided paraphrase generation model that leverages graph GRU to encode the coherency relationship graph and get the cohesion-aware representation for each sentence. |
| Outcome: | The proposed model outperforms baseline models on BERTScore and diversity scores. |