@inproceedings{jalili-sabet-etal-2016-improving,
title = "Improving Word Alignment of Rare Words with Word Embeddings",
author = "Jalili Sabet, Masoud and
Faili, Heshaam and
Haffari, Gholamreza",
editor = "Matsumoto, Yuji and
Prasad, Rashmi",
booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
month = dec,
year = "2016",
address = "Osaka, Japan",
publisher = "The COLING 2016 Organizing Committee",
url = "https://aclanthology.org/C16-1302",
pages = "3209--3215",
abstract = "We address the problem of inducing word alignment for language pairs by developing an unsupervised model with the capability of getting applied to other generative alignment models. We approach the task by: i)proposing a new alignment model based on the IBM alignment model 1 that uses vector representation of words, and ii)examining the use of similar source words to overcome the problem of rare source words and improving the alignments. We apply our method to English-French corpora and run the experiments with different sizes of sentence pairs. Our results show competitive performance against the baseline and in some cases improve the results up to 6.9{\%} in terms of precision.",
}
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%0 Conference Proceedings
%T Improving Word Alignment of Rare Words with Word Embeddings
%A Jalili Sabet, Masoud
%A Faili, Heshaam
%A Haffari, Gholamreza
%Y Matsumoto, Yuji
%Y Prasad, Rashmi
%S Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers
%D 2016
%8 December
%I The COLING 2016 Organizing Committee
%C Osaka, Japan
%F jalili-sabet-etal-2016-improving
%X We address the problem of inducing word alignment for language pairs by developing an unsupervised model with the capability of getting applied to other generative alignment models. We approach the task by: i)proposing a new alignment model based on the IBM alignment model 1 that uses vector representation of words, and ii)examining the use of similar source words to overcome the problem of rare source words and improving the alignments. We apply our method to English-French corpora and run the experiments with different sizes of sentence pairs. Our results show competitive performance against the baseline and in some cases improve the results up to 6.9% in terms of precision.
%U https://aclanthology.org/C16-1302
%P 3209-3215
Markdown (Informal)
[Improving Word Alignment of Rare Words with Word Embeddings](https://aclanthology.org/C16-1302) (Jalili Sabet et al., COLING 2016)
ACL
- Masoud Jalili Sabet, Heshaam Faili, and Gholamreza Haffari. 2016. Improving Word Alignment of Rare Words with Word Embeddings. In Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, pages 3209–3215, Osaka, Japan. The COLING 2016 Organizing Committee.