Shuyue Stella Li


2024

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Narrowing the Gap between Zero- and Few-shot Machine Translation by Matching Styles
Weiting Tan | Haoran Xu | Lingfeng Shen | Shuyue Stella Li | Kenton Murray | Philipp Koehn | Benjamin Van Durme | Yunmo Chen
Findings of the Association for Computational Linguistics: NAACL 2024

Large language models trained primarily in a monolingual setting have demonstrated their ability to generalize to machine translation using zero- and few-shot examples with in-context learning. However, even though zero-shot translations are relatively good, there remains a discernible gap comparing their performance with the few-shot setting. In this paper, we investigate the factors contributing to this gap and find that this gap can largely be closed (for about 70%) by matching the writing styles of the target corpus. Additionally, we explore potential approaches to enhance zero-shot baselines without the need for parallel demonstration examples, providing valuable insights into how these methods contribute to improving translation metrics.

2023

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A Quantitative Approach to Understand Self-Supervised Models as Cross-lingual Feature Extracters
Shuyue Stella Li | Beining Xu | Xiangyu Zhang | Hexin Liu | Wenhan Chao | Paola Garcia
Proceedings of the 6th International Conference on Natural Language and Speech Processing (ICNLSP 2023)

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Learning from Mistakes: Towards Robust Neural Machine Translation for Disfluent L2 Sentences
Shuyue Stella Li | Philipp Koehn
Proceedings of Machine Translation Summit XIX, Vol. 1: Research Track

We study the sentences written by second-language (L2) learners to improve the robustness of current neural machine translation (NMT) models on this type of data. Current large datasets used to train NMT systems are mostly Wikipedia or government documents written by highly competent speakers of that language, especially English. However, given that English is the most common second language, it is crucial that machine translation systems are robust against the large number of sentences written by L2 learners of English. By studying the difficulties faced by humans in their L2 acquisition process, we are able to transfer such insights to machine translation systems to recover from source-side fluency variations. In this work, we create additional training data with artificial errors similar to mistakes made by L2 learners of various fluency levels to improve the quality of the machine translation system. We test our method in zero-shot settings on the JFLEG-es (English-Spanish) dataset. The quality of our machine translation system on disfluent sentences outperforms the baseline by 1.8 BLEU scores.