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Machine Translation

, Volume 32, Issue 1–2, pp 143–165 | Cite as

Cross-lingual sentiment transfer with limited resources

  • Mohammad Sadegh Rasooli
  • Noura Farra
  • Axinia Radeva
  • Tao Yu
  • Kathleen McKeown
Article

Abstract

We describe two transfer approaches for building sentiment analysis systems without having gold labeled data in the target language. Unlike previous work that is focused on using only English as the source language and a small number of target languages, we use multiple source languages to learn a more robust sentiment transfer model for 16 languages from different language families. Our approaches explore the potential of using an annotation projection approach and a direct transfer approach using cross-lingual word representations and neural networks. Whereas most previous work relies on machine translation, we show that we can build cross-lingual sentiment analysis systems without machine translation or even high quality parallel data. We have conducted experiments assessing the availability of different resources such as in-domain parallel data, out-of-domain parallel data, and in-domain comparable data. Our experiments show that we can build a robust transfer system whose performance can in some cases approach that of a supervised system.

Keywords

Cross-lingual sentiment Direct transfer Annotation projection Low-resource 

Notes

Acknowledgements

Noura Farra, Kathleen McKeown and Axinia Radeva were supported by DARPA LORELEI Grant HR0011-15-2-0041. Kathleen McKeown and Tao Yu were supported by DARPA DEFT Grant FA8750-12-2-0347. The views expressed are those of the authors and do not reflect the official policy or position of the Department of Defense or the U.S government. We thank the reviewers for their detailed and helpful comments. We thank the Uyghur native informant and Appen for arranging the annotation. We thank Zixiaofan (Brenda) Yang for preparing the Uyghur evaluation data and meeting the informant.

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Copyright information

© Springer Science+Business Media B.V. 2017

Authors and Affiliations

  1. 1.Department of Computer ScienceColumbia UniversityNew YorkUSA
  2. 2.Department of Computer ScienceYale UniversityNew HavenUSA

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