Papers by Lingling Mu
Multi-pass Decoding for Grammatical Error Correction (2024.emnlp-main)
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| Challenge: | Seq2edit models decode only once without aware of subsequent tokens. |
| Approach: | They propose to iteratively refine the correction results of seq2seq models via Multi-Pass Decoding (MPD) to improve performance, but MPD increases inference costs . they propose to merge the source input and previous round correction result into one sequence. |
| Outcome: | Experiments on the CoNLL-14 and BEA-19 test set show that the proposed approach improves over baselines. |
A Self-Distillation Recipe for Neural Machine Translation (2025.findings-acl)
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| Challenge: | Existing methods for Neural Machine Translation (NMT) have been proven effective in improving the performance of computer vision tasks without pre-training a teacher. |
| Approach: | They propose a rank-order augmented Pearson correlation loss and an iterative distillation method to prevent the discrepancy of predictions between the student and a stronger teacher from disturbing the training. |
| Outcome: | The proposed method can lead to significant improvements over the strong Transformer baseline on low/middle/high-resource tasks, obtaining comparable or better performance with fewer layers. |
JOLT-SQL: Joint Loss Tuning of Text-to-SQL with Confusion-aware Noisy Schema Sampling (2025.emnlp-main)
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| Challenge: | Recent advances in Large Language Models have improved Text-to-SQL methods . however, they still face challenges such as complex multi-stage pipelines and poor robustness to noisy schema information. |
| Approach: | They propose a single-stage SFT framework that optimizes schema linking and SQL generation via a unified loss. |
| Outcome: | Experiments on the Spider and BIRD benchmarks show that JOLT-SQL achieves state-of-the-art execution accuracy among comparable-size open-source models. |
ParaZh-22M: A Large-Scale Chinese Parabank via Machine Translation (2022.coling-1)
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| Challenge: | Paraphrasing is an important data augmentation approach for natural language processing (NLP). |
| Approach: | They propose to extract sentence-level paraphrases from multiple Chinese translations and construct a larger Chinese parabank with 22M sentence pairs. |
| Outcome: | The proposed parabank is the largest to date in Chinese, but limited by one-to-many translation data. |