 |
Communication dans un congrès
SimAlign: High Quality Word Alignments Without Parallel Training Data Using Static and Contextualized Embeddings
Abstract : Word alignments are useful for tasks like statistical and neural machine translation (NMT) and cross-lingual annotation projection. Statistical word aligners perform well, as do methods that extract alignments jointly with translations in NMT. However, most approaches require parallel training data and quality decreases as less training data is available. We propose word alignment methods that require no parallel data. The key idea is to leverage multilingual word embeddings {--} both static and contextualized {--} for word alignment. Our multilingual embeddings are created from monolingual data only without relying on any parallel data or dictionaries. We find that alignments created from embeddings are superior for four and comparable for two language pairs compared to those produced by traditional statistical aligners {--} even with abundant parallel data; e.g., contextualized embeddings achieve a word alignment F1 for English-German that is 5 percentage points higher than eflomal, a high-quality statistical aligner, trained on 100k parallel sentences.
https://hal.archives-ouvertes.fr/hal-03013194
Contributeur : Limsi Publications <>
Soumis le : jeudi 19 novembre 2020 - 15:23:09
Dernière modification le : lundi 22 février 2021 - 16:21:22
Archivage à long terme le : : samedi 20 février 2021 - 19:03:21
Contributeur : Limsi Publications <>
Soumis le : jeudi 19 novembre 2020 - 15:23:09
Dernière modification le : lundi 22 février 2021 - 16:21:22
Archivage à long terme le : : samedi 20 février 2021 - 19:03:21
Fichier
2020.findings-emnlp.147.pdf
Fichiers éditeurs autorisés sur une archive ouverte