@inproceedings{stengel-eskin-etal-2019-discriminative,
title = "A Discriminative Neural Model for Cross-Lingual Word Alignment",
author = "Stengel-Eskin, Elias and
Su, Tzu-ray and
Post, Matt and
Van Durme, Benjamin",
editor = "Inui, Kentaro and
Jiang, Jing and
Ng, Vincent and
Wan, Xiaojun",
booktitle = "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 = nov,
year = "2019",
address = "Hong Kong, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D19-1084",
doi = "10.18653/v1/D19-1084",
pages = "910--920",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T A Discriminative Neural Model for Cross-Lingual Word Alignment
%A Stengel-Eskin, Elias
%A Su, Tzu-ray
%A Post, Matt
%A Van Durme, Benjamin
%Y Inui, Kentaro
%Y Jiang, Jing
%Y Ng, Vincent
%Y Wan, Xiaojun
%S Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
%D 2019
%8 November
%I Association for Computational Linguistics
%C Hong Kong, China
%F stengel-eskin-etal-2019-discriminative
%X 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.
%R 10.18653/v1/D19-1084
%U https://aclanthology.org/D19-1084
%U https://doi.org/10.18653/v1/D19-1084
%P 910-920
Markdown (Informal)
[A Discriminative Neural Model for Cross-Lingual Word Alignment](https://aclanthology.org/D19-1084) (Stengel-Eskin et al., EMNLP-IJCNLP 2019)
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.