SimAlign: High Quality Word Alignments Without Parallel Training Data Using Static and Contextualized Embeddings

Masoud Jalili Sabet, Philipp Dufter, François Yvon, Hinrich Schütze


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.
Anthology ID:
2020.findings-emnlp.147
Volume:
Findings of the Association for Computational Linguistics: EMNLP 2020
Month:
November
Year:
2020
Address:
Online
Editors:
Trevor Cohn, Yulan He, Yang Liu
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1627–1643
Language:
URL:
https://aclanthology.org/2020.findings-emnlp.147
DOI:
10.18653/v1/2020.findings-emnlp.147
Bibkey:
Cite (ACL):
Masoud Jalili Sabet, Philipp Dufter, François Yvon, and Hinrich Schütze. 2020. SimAlign: High Quality Word Alignments Without Parallel Training Data Using Static and Contextualized Embeddings. In Findings of the Association for Computational Linguistics: EMNLP 2020, pages 1627–1643, Online. Association for Computational Linguistics.
Cite (Informal):
SimAlign: High Quality Word Alignments Without Parallel Training Data Using Static and Contextualized Embeddings (Jalili Sabet et al., Findings 2020)
Copy Citation:
PDF:
https://preview.aclanthology.org/nschneid-patch-1/2020.findings-emnlp.147.pdf
Video:
 https://slideslive.com/38940631
Code
 masoudjs/simalign +  additional community code