Papers by Alison Lui
Neural Machine Translation of Text from Non-Native Speakers (N19-1)
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| Challenge: | Neural Machine Translation (NMT) systems are known to degrade when confronted with noisy data. |
| Approach: | They propose to augment training data with sentences containing artificially-introduced grammatical errors to make the system more robust to such errors. |
| Outcome: | The proposed approach recovers 1.0 BLEU out of 2.4 BLUE lost due to grammatical errors on a set of Spanish translations of the JFLEG grammar error correction corpus. |
WMT24++: Expanding the Language Coverage of WMT24 to 55 Languages & Dialects (2025.findings-acl)
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Daniel Deutsch, Eleftheria Briakou, Isaac Rayburn Caswell, Mara Finkelstein, Rebecca Galor, Juraj Juraska, Geza Kovacs, Alison Lui, Ricardo Rei, Jason Riesa, Shruti Rijhwani, Parker Riley, Elizabeth Salesky, Firas Trabelsi, Stephanie Winkler, Biao Zhang, Markus Freitag
| Challenge: | In order to evaluate large language models (LLMs), it is important to collect benchmark datasets in order to assess their multilingual performance. |
| Approach: | They extend the WMT24 dataset to cover 55 languages by collecting new human-written references and post-edits for 46 new languages/dialects. |
| Outcome: | The proposed dataset covers 55 languages and provides best-performing MT systems in all 55 languages. |