A Discriminative Neural Model for Cross-Lingual Word Alignment

Elias Stengel-Eskin, Tzu-ray Su, Matt Post, Benjamin Van Durme

Abstract
We introduce a novel discriminative word alignment model, which we integrate into a Transformer-based machine translation model. In experiments based on a small number of labeled examples (∼1.7K–5K sentences) we evaluate its performance intrinsically on both English-Chinese and English-Arabic alignment, where we achieve major improvements over unsupervised baselines (11–27 F1). We evaluate the model extrinsically on data projection for Chinese NER, showing that our alignments lead to higher performance when used to project NER tags from English to Chinese. Finally, we perform an ablation analysis and an annotation experiment that jointly support the utility and feasibility of future manual alignment elicitation.
Anthology ID:
D19-1084
Volume:
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
Month:
November
Year:
2019
Address:
Hong Kong, China
Editors:
Kentaro Inui, Jing Jiang, Vincent Ng, Xiaojun Wan
Venues:
EMNLP | IJCNLP
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
910–920
Language:
URL:
https://aclanthology.org/D19-1084
DOI:
10.18653/v1/D19-1084
Bibkey:
Cite (ACL):
Elias Stengel-Eskin, Tzu-ray Su, Matt Post, and Benjamin Van Durme. 2019. A Discriminative Neural Model for Cross-Lingual Word Alignment. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 910–920, Hong Kong, China. Association for Computational Linguistics.
Cite (Informal):
A Discriminative Neural Model for Cross-Lingual Word Alignment (Stengel-Eskin et al., EMNLP-IJCNLP 2019)
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https://preview.aclanthology.org/nschneid-patch-4/D19-1084.pdf
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