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        <title>Improving Sentiment Analysis over non-English Tweets using Multilingual Transformers and Automatic Translation for Data-Augmentation</title>
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        <namePart type="given">Valentin</namePart>
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    <name type="personal">
        <namePart type="given">Alexandra</namePart>
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            <title>Proceedings of the 28th International Conference on Computational Linguistics</title>
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            <namePart type="given">Donia</namePart>
            <namePart type="family">Scott</namePart>
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        <name type="personal">
            <namePart type="given">Nuria</namePart>
            <namePart type="family">Bel</namePart>
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        <name type="personal">
            <namePart type="given">Chengqing</namePart>
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    <abstract>Tweets are specific text data when compared to general text. Although sentiment analysis over tweets has become very popular in the last decade for English, it is still difficult to find huge annotated corpora for non-English languages. The recent rise of the transformer models in Natural Language Processing allows to achieve unparalleled performances in many tasks, but these models need a consequent quantity of text to adapt to the tweet domain. We propose the use of a multilingual transformer model, that we pre-train over English tweets on which we apply data-augmentation using automatic translation to adapt the model to non-English languages. Our experiments in French, Spanish, German and Italian suggest that the proposed technique is an efficient way to improve the results of the transformers over small corpora of tweets in a non-English language.</abstract>
    <identifier type="citekey">barriere-balahur-2020-improving</identifier>
    <identifier type="doi">10.18653/v1/2020.coling-main.23</identifier>
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%0 Conference Proceedings
%T Improving Sentiment Analysis over non-English Tweets using Multilingual Transformers and Automatic Translation for Data-Augmentation
%A Barriere, Valentin
%A Balahur, Alexandra
%Y Scott, Donia
%Y Bel, Nuria
%Y Zong, Chengqing
%S Proceedings of the 28th International Conference on Computational Linguistics
%D 2020
%8 December
%I International Committee on Computational Linguistics
%C Barcelona, Spain (Online)
%F barriere-balahur-2020-improving
%X Tweets are specific text data when compared to general text. Although sentiment analysis over tweets has become very popular in the last decade for English, it is still difficult to find huge annotated corpora for non-English languages. The recent rise of the transformer models in Natural Language Processing allows to achieve unparalleled performances in many tasks, but these models need a consequent quantity of text to adapt to the tweet domain. We propose the use of a multilingual transformer model, that we pre-train over English tweets on which we apply data-augmentation using automatic translation to adapt the model to non-English languages. Our experiments in French, Spanish, German and Italian suggest that the proposed technique is an efficient way to improve the results of the transformers over small corpora of tweets in a non-English language.
%R 10.18653/v1/2020.coling-main.23
%U https://aclanthology.org/2020.coling-main.23
%U https://doi.org/10.18653/v1/2020.coling-main.23
%P 266-271

Markdown (Informal)

[Improving Sentiment Analysis over non-English Tweets using Multilingual Transformers and Automatic Translation for Data-Augmentation](https://aclanthology.org/2020.coling-main.23) (Barriere & Balahur, COLING 2020)

• Improving Sentiment Analysis over non-English Tweets using Multilingual Transformers and Automatic Translation for Data-Augmentation (Barriere & Balahur, COLING 2020)

ACL

• Valentin Barriere and Alexandra Balahur. 2020. Improving Sentiment Analysis over non-English Tweets using Multilingual Transformers and Automatic Translation for Data-Augmentation. In Proceedings of the 28th International Conference on Computational Linguistics, pages 266–271, Barcelona, Spain (Online). International Committee on Computational Linguistics.