@inproceedings{hu-etal-2019-improved,
title = "Improved Lexically Constrained Decoding for Translation and Monolingual Rewriting",
author = "Hu, J. Edward and
Khayrallah, Huda and
Culkin, Ryan and
Xia, Patrick and
Chen, Tongfei and
Post, Matt and
Van Durme, Benjamin",
editor = "Burstein, Jill and
Doran, Christy and
Solorio, Thamar",
booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
month = jun,
year = "2019",
address = "Minneapolis, Minnesota",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/N19-1090",
doi = "10.18653/v1/N19-1090",
pages = "839--850",
abstract = "Lexically-constrained sequence decoding allows for explicit positive or negative phrase-based constraints to be placed on target output strings in generation tasks such as machine translation or monolingual text rewriting. We describe vectorized dynamic beam allocation, which extends work in lexically-constrained decoding to work with batching, leading to a five-fold improvement in throughput when working with positive constraints. Faster decoding enables faster exploration of constraint strategies: we illustrate this via data augmentation experiments with a monolingual rewriter applied to the tasks of natural language inference, question answering and machine translation, showing improvements in all three.",
}
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%0 Conference Proceedings
%T Improved Lexically Constrained Decoding for Translation and Monolingual Rewriting
%A Hu, J. Edward
%A Khayrallah, Huda
%A Culkin, Ryan
%A Xia, Patrick
%A Chen, Tongfei
%A Post, Matt
%A Van Durme, Benjamin
%Y Burstein, Jill
%Y Doran, Christy
%Y Solorio, Thamar
%S Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)
%D 2019
%8 June
%I Association for Computational Linguistics
%C Minneapolis, Minnesota
%F hu-etal-2019-improved
%X Lexically-constrained sequence decoding allows for explicit positive or negative phrase-based constraints to be placed on target output strings in generation tasks such as machine translation or monolingual text rewriting. We describe vectorized dynamic beam allocation, which extends work in lexically-constrained decoding to work with batching, leading to a five-fold improvement in throughput when working with positive constraints. Faster decoding enables faster exploration of constraint strategies: we illustrate this via data augmentation experiments with a monolingual rewriter applied to the tasks of natural language inference, question answering and machine translation, showing improvements in all three.
%R 10.18653/v1/N19-1090
%U https://aclanthology.org/N19-1090
%U https://doi.org/10.18653/v1/N19-1090
%P 839-850
Markdown (Informal)
[Improved Lexically Constrained Decoding for Translation and Monolingual Rewriting](https://aclanthology.org/N19-1090) (Hu et al., NAACL 2019)
ACL
- J. Edward Hu, Huda Khayrallah, Ryan Culkin, Patrick Xia, Tongfei Chen, Matt Post, and Benjamin Van Durme. 2019. Improved Lexically Constrained Decoding for Translation and Monolingual Rewriting. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pages 839–850, Minneapolis, Minnesota. Association for Computational Linguistics.