Papers by Raphael Rubino
A Multilingual Multiway Evaluation Data Set for Structured Document Translation of Asian Languages (2022.findings-aacl)
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| Challenge: | a lack of evaluation data sets for structured content limits progress in machine translation . a common use case of machine translation is the translation of structured or formatted documents . |
| Approach: | They propose a multilingual multiway evaluation data set for machine translation of structured documents of Asian languages Japanese, Korean and Chinese. |
| Outcome: | The proposed data set is well suited for multilingual evaluation and contains richer annotation tag sets than existing data sets. |
Scientific Credibility of Machine Translation Research: A Meta-Evaluation of 769 Papers (2021.acl-long)
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| Challenge: | a meta-evaluation of machine translation (MT) has been conducted in 769 research papers . a recent study shows that evaluation practices have changed over the past decade . |
| Approach: | They propose a meta-evaluation method for machine translation that uses BLEU scores to evaluate MT performance. |
| Outcome: | The proposed meta-evaluation of machine translation shows that evaluation practices have changed over the past decade . the authors suggest that the evaluation process should be streamlined and standardized to ensure the validity of the evaluation method . |
Normalizing without Modernizing: Keeping Historical Wordforms of Middle French while Reducing Spelling Variants (2024.findings-naacl)
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| Challenge: | a new method to normalize orthographic variations of historical documents is needed for digital humanities and diachronic studies. |
| Approach: | They propose to normalize orthographic wordforms found in Middle French archives . authors say it improves accuracy and accuracy over a strong baseline . |
| Outcome: | The proposed methods normalize orthographic variations of historical documents without modernizing them. |
Intermediate Self-supervised Learning for Machine Translation Quality Estimation (2020.coling-main)
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| Challenge: | Existing methods for machine translation quality estimation (QE) rely on annotated data. |
| Approach: | They propose a self-supervised learning task for machine translation (MT) that orients a pre-trained model towards the target task. |
| Outcome: | The proposed method outperforms existing methods on English-to-German and English- to-Russian translation directions and is comparable to existing models. |
Tagged Back-translation Revisited: Why Does It Really Work? (2020.acl-main)
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| Challenge: | In this paper, we show that neural machine translation systems trained on large back-translated data overfit some of the characteristics of machine-transcribed texts. |
| Approach: | They propose to add a tag to back-translations to help distinguish back-translated data from original parallel training data. |
| Outcome: | The proposed tag helps the system distinguish back-translated data from original parallel training data and is as effective as a tag in high-resource training. |