| Téléchargement | - Voir le manuscrit accepté : Improving parallel data identification using iteratively refined sentence alignments and bilingual mappings of pre-trained language models (PDF, 382 Kio)
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| Auteur | Rechercher : Lo, Chi-Kiu1; Rechercher : Joanis, Eric1 |
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| Affiliation | - Conseil national de recherches du Canada. Technologies numériques
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| Format | Texte, Article |
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| Conférence | The 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020), November 19-20, 2020 [Held Online] |
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| Résumé | The National Research Council of Canada’s team submissions to the parallel corpus filtering task at the Fifth Conference on Machine Translation are based on two key components: (1) iteratively refined statistical sentence alignments for extracting sentence pairs from document pairs and (2) a crosslingual semantic textual similarity metric based on a pretrained multilingual language model, XLMRoBERTa, with bilingual mappings learnt from a minimal amount of clean parallel data for scoring the parallelism of the extracted sentence pairs. The translation quality of the neural machine translation systems trained and fine-tuned on the parallel data extracted by our submissions improved significantly when compared to the organizers’ LASER-based baseline, a sentence-embedding method that worked well last year. For re-aligning the sentences in the document pairs (component 1), our statistical approach has outperformed the current state-of-the-art neural approach in this low-resource context. |
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| Date de publication | 2020-11-19 |
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| Date de création | 2020-11-30 |
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| Maison d’édition | Association for Computational Linguistics |
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| Dans | |
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| Langue | anglais |
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| Publications évaluées par des pairs | Oui |
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| Exporter la notice | Exporter en format RIS |
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| Signaler une correction | Signaler une correction (s'ouvre dans un nouvel onglet) |
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| Identificateur de l’enregistrement | 520de843-ed04-446c-83cc-d121adf6aa90 |
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| Enregistrement créé | 2020-11-30 |
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| Enregistrement modifié | 2020-11-30 |
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