@inproceedings{tebbifakhr-etal-2019-machine,
title = "Machine Translation for Machines: the Sentiment Classification Use Case",
author = "Tebbifakhr, Amirhossein and
Bentivogli, Luisa and
Negri, Matteo and
Turchi, Marco",
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/D19-1140",
doi = "10.18653/v1/D19-1140",
pages = "1368--1374",
abstract = "We propose a neural machine translation (NMT) approach that, instead of pursuing adequacy and fluency ({``}human-oriented{''} quality criteria), aims to generate translations that are best suited as input to a natural language processing component designed for a specific downstream task (a {``}machine-oriented{''} criterion). Towards this objective, we present a reinforcement learning technique based on a new candidate sampling strategy, which exploits the results obtained on the downstream task as weak feedback. Experiments in sentiment classification of Twitter data in German and Italian show that feeding an English classifier with {``}machine-oriented{''} translations significantly improves its performance. Classification results outperform those obtained with translations produced by general-purpose NMT models as well as by an approach based on reinforcement learning. Moreover, our results on both languages approximate the classification accuracy computed on gold standard English tweets.",
}
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<abstract>We propose a neural machine translation (NMT) approach that, instead of pursuing adequacy and fluency (“human-oriented” quality criteria), aims to generate translations that are best suited as input to a natural language processing component designed for a specific downstream task (a “machine-oriented” criterion). Towards this objective, we present a reinforcement learning technique based on a new candidate sampling strategy, which exploits the results obtained on the downstream task as weak feedback. Experiments in sentiment classification of Twitter data in German and Italian show that feeding an English classifier with “machine-oriented” translations significantly improves its performance. Classification results outperform those obtained with translations produced by general-purpose NMT models as well as by an approach based on reinforcement learning. Moreover, our results on both languages approximate the classification accuracy computed on gold standard English tweets.</abstract>
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%0 Conference Proceedings
%T Machine Translation for Machines: the Sentiment Classification Use Case
%A Tebbifakhr, Amirhossein
%A Bentivogli, Luisa
%A Negri, Matteo
%A Turchi, Marco
%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 tebbifakhr-etal-2019-machine
%X We propose a neural machine translation (NMT) approach that, instead of pursuing adequacy and fluency (“human-oriented” quality criteria), aims to generate translations that are best suited as input to a natural language processing component designed for a specific downstream task (a “machine-oriented” criterion). Towards this objective, we present a reinforcement learning technique based on a new candidate sampling strategy, which exploits the results obtained on the downstream task as weak feedback. Experiments in sentiment classification of Twitter data in German and Italian show that feeding an English classifier with “machine-oriented” translations significantly improves its performance. Classification results outperform those obtained with translations produced by general-purpose NMT models as well as by an approach based on reinforcement learning. Moreover, our results on both languages approximate the classification accuracy computed on gold standard English tweets.
%R 10.18653/v1/D19-1140
%U https://aclanthology.org/D19-1140
%U https://doi.org/10.18653/v1/D19-1140
%P 1368-1374
Markdown (Informal)
[Machine Translation for Machines: the Sentiment Classification Use Case](https://aclanthology.org/D19-1140) (Tebbifakhr et al., EMNLP-IJCNLP 2019)
ACL
- Amirhossein Tebbifakhr, Luisa Bentivogli, Matteo Negri, and Marco Turchi. 2019. Machine Translation for Machines: the Sentiment Classification Use Case. 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 1368–1374, Hong Kong, China. Association for Computational Linguistics.