Papers by Ran Cheng
MReD: A Meta-Review Dataset for Structure-Controllable Text Generation (2022.findings-acl)
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| Challenge: | a new text generation dataset is needed to controllable text summarization, but it lacks the domain knowledge. |
| Approach: | They propose to use existing text generation datasets to leverage input and control signals . they propose to annotate each meta-review sentence manually with a control signal . |
| Outcome: | The proposed method can be used to control the structure of a text generation dataset . it can be applied to a variety of tasks, including a task with a large number of meta-review sentences . |
HiTab: A Hierarchical Table Dataset for Question Answering and Natural Language Generation (2022.acl-long)
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Zhoujun Cheng, Haoyu Dong, Zhiruo Wang, Ran Jia, Jiaqi Guo, Yan Gao, Shi Han, Jian-Guang Lou, Dongmei Zhang
| Challenge: | Existing studies on table reasoning focus on flat tables and hierarchical tables . a new dataset, HiTab, aims to examine numerical reasoning over hierarchic tables based on hierarchically structured tables - a strong challenge for existing baselines and a valuable benchmark for future research. |
| Approach: | They propose a hierarchical question answering and natural language generation dataset to study hierarchic tables. |
| Outcome: | The proposed model shows that it is effective in QA and natural language generation over hierarchical tables. |
Beyond Binary Preferences: Semi-Online Label-Free GRACE-KTO with Group-Wise Adaptive Calibration for High-Quality Long-Text Generation (2025.findings-emnlp)
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| Challenge: | Generating high-quality long-text remains challenging for Large Language Models (LLMs), as conventional supervised fine-tuning fails to ensure overall quality due to its teacher-forcing nature. |
| Approach: | They propose a semi-online framework that transforms KTO’s binary signals into dynamically calibrated intra-group rewards. |
| Outcome: | The proposed framework transforms binary signals into dynamically calibrated intra-group rewards. |
FORTAP: Using Formulas for Numerical-Reasoning-Aware Table Pretraining (2022.acl-long)
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| Challenge: | Tables store rich numerical data, but numerical reasoning over tables is still a challenge. |
| Approach: | They propose a spreadsheet formula is a valuable supervision for numerical reasoning in tables. |
| Outcome: | The proposed method outperforms state-of-the-art methods on three representative datasets of formula prediction, question answering, and cell type classification. |
Revisiting Self-training for Few-shot Learning of Language Model (2021.emnlp-main)
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| Challenge: | Unlabeled data are useful for few-shot learning of language models. |
| Approach: | They propose a prompt-based few-shot learner that uses unlabeled data to fine-tune language models. |
| Outcome: | The proposed approach outperforms state-of-the-art models on six sentence classification and six sentence-pair classification benchmarking tasks. |
Wav2SQL: Direct Generalizable Speech-To-SQL Parsing (2024.findings-acl)
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| Challenge: | Existing models for speech-driven SQL parsing are based on a cascaded approach, resulting in data scarcity and inconsistent performance. |
| Approach: | They propose a direct generalizable speech-to-SQL parsing model which avoids error compounding across cascaded systems. |
| Outcome: | The proposed model avoids error compounding and achieves state-of-the-art results by 4.7% improvement over baseline. |