Papers with translation metrics
Narrowing the Gap between Zero- and Few-shot Machine Translation by Matching Styles (2024.findings-naacl)
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Weiting Tan, Haoran Xu, Lingfeng Shen, Shuyue Stella Li, Kenton Murray, Philipp Koehn, Benjamin Van Durme, Yunmo Chen
| Challenge: | Recent work shows that large language models can generalize to machine translation using zero-shot examples with in-context learning. |
| Approach: | They investigate the factors contributing to this gap by matching the writing styles of the target corpus. |
| Outcome: | The proposed methods can be enhanced without the need for parallel demonstration examples. |
Towards Example-Based NMT with Multi-Levenshtein Transformers (2023.emnlp-main)
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| Challenge: | Retrieval-augmented machine translation (RAMT) is attracting growing attention . it is assumed to implement some form of domain adaptation . |
| Approach: | They propose a retrieval-augmented version of the Levenshtein Transformer to make it more transparent . they propose to perform training and inference in this model, based on multi-way alignment algorithms and imitation learning. |
| Outcome: | The proposed architecture improves translation performance and improves consistency of translations compared to previous models. |
Fixing Rogue Memorization in Many-to-One Multilingual Translators of Extremely-Low-Resource Languages by Rephrasing Training Samples (2024.naacl-long)
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| Challenge: | Existing fine-tuning of large high-resource language models into multilingual machine translators is difficult for extremely lowresource languages. |
| Approach: | They propose to fine-tune large high-resource language models into multilingual machine translators for extremely-lowresource languages such as endangered Indigenous languages. |
| Outcome: | The proposed model halls are reformulated to improve translation accuracy and improve translation quality. |
XQ-MEval: A Dataset with Cross-lingual Parallel Quality for Benchmarking Translation Metrics (2026.findings-acl)
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| Challenge: | averaging metric scores across languages is suspicious since translations of equal quality receive different scores across language. |
| Approach: | They propose a semi-automatically built dataset to benchmark translation metrics using MQM-defined errors and a normalization strategy to mitigate cross-lingual scoring bias. |
| Outcome: | The proposed model shows that translation metrics suffer from cross-lingual scoring bias . the proposed model is based on a semi-automatically built dataset covering nine translation directions . |