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        <title>Did you offend me? Classification of Offensive Tweets in Hinglish Language</title>
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            <title>Proceedings of the 2nd Workshop on Abusive Language Online (ALW2)</title>
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            <namePart type="given">Darja</namePart>
            <namePart type="family">Fišer</namePart>
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            <namePart type="given">Ruihong</namePart>
            <namePart type="family">Huang</namePart>
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            <namePart type="given">Vinodkumar</namePart>
            <namePart type="family">Prabhakaran</namePart>
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    <abstract>The use of code-switched languages (e.g., Hinglish, which is derived by the blending of Hindi with the English language) is getting much popular on Twitter due to their ease of communication in native languages. However, spelling variations and absence of grammar rules introduce ambiguity and make it difficult to understand the text automatically. This paper presents the Multi-Input Multi-Channel Transfer Learning based model (MIMCT) to detect offensive (hate speech or abusive) Hinglish tweets from the proposed Hinglish Offensive Tweet (HOT) dataset using transfer learning coupled with multiple feature inputs. Specifically, it takes multiple primary word embedding along with secondary extracted features as inputs to train a multi-channel CNN-LSTM architecture that has been pre-trained on English tweets through transfer learning. The proposed MIMCT model outperforms the baseline supervised classification models, transfer learning based CNN and LSTM models to establish itself as the state of the art in the unexplored domain of Hinglish offensive text classification.</abstract>
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%0 Conference Proceedings
%T Did you offend me? Classification of Offensive Tweets in Hinglish Language
%A Mathur, Puneet
%A Sawhney, Ramit
%A Ayyar, Meghna
%A Shah, Rajiv
%Y Fišer, Darja
%Y Huang, Ruihong
%Y Prabhakaran, Vinodkumar
%Y Voigt, Rob
%Y Waseem, Zeerak
%Y Wernimont, Jacqueline
%S Proceedings of the 2nd Workshop on Abusive Language Online (ALW2)
%D 2018
%8 October
%I Association for Computational Linguistics
%C Brussels, Belgium
%F mathur-etal-2018-offend
%X The use of code-switched languages (e.g., Hinglish, which is derived by the blending of Hindi with the English language) is getting much popular on Twitter due to their ease of communication in native languages. However, spelling variations and absence of grammar rules introduce ambiguity and make it difficult to understand the text automatically. This paper presents the Multi-Input Multi-Channel Transfer Learning based model (MIMCT) to detect offensive (hate speech or abusive) Hinglish tweets from the proposed Hinglish Offensive Tweet (HOT) dataset using transfer learning coupled with multiple feature inputs. Specifically, it takes multiple primary word embedding along with secondary extracted features as inputs to train a multi-channel CNN-LSTM architecture that has been pre-trained on English tweets through transfer learning. The proposed MIMCT model outperforms the baseline supervised classification models, transfer learning based CNN and LSTM models to establish itself as the state of the art in the unexplored domain of Hinglish offensive text classification.
%R 10.18653/v1/W18-5118
%U https://aclanthology.org/W18-5118
%U https://doi.org/10.18653/v1/W18-5118
%P 138-148

Markdown (Informal)

[Did you offend me? Classification of Offensive Tweets in Hinglish Language](https://aclanthology.org/W18-5118) (Mathur et al., ALW 2018)

• Did you offend me? Classification of Offensive Tweets in Hinglish Language (Mathur et al., ALW 2018)

ACL

• Puneet Mathur, Ramit Sawhney, Meghna Ayyar, and Rajiv Shah. 2018. Did you offend me? Classification of Offensive Tweets in Hinglish Language. In Proceedings of the 2nd Workshop on Abusive Language Online (ALW2), pages 138–148, Brussels, Belgium. Association for Computational Linguistics.