@inproceedings{sane-etal-2019-deep,
title = "Deep Learning Techniques for Humor Detection in {H}indi-{E}nglish Code-Mixed Tweets",
author = "Sane, Sushmitha Reddy and
Tripathi, Suraj and
Sane, Koushik Reddy and
Mamidi, Radhika",
editor = "Balahur, Alexandra and
Klinger, Roman and
Hoste, Veronique and
Strapparava, Carlo and
De Clercq, Orphee",
booktitle = "Proceedings of the Tenth Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis",
month = jun,
year = "2019",
address = "Minneapolis, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W19-1307",
doi = "10.18653/v1/W19-1307",
pages = "57--61",
abstract = "We propose bilingual word embeddings based on word2vec and fastText models (CBOW and Skip-gram) to address the problem of Humor detection in Hindi-English code-mixed tweets in combination with deep learning architectures. We focus on deep learning approaches which are not widely used on code-mixed data and analyzed their performance by experimenting with three different neural network models. We propose convolution neural network (CNN) and bidirectional long-short term memory (biLSTM) (with and without Attention) models which take the generated bilingual embeddings as input. We make use of Twitter data to create bilingual word embeddings. All our proposed architectures outperform the state-of-the-art results, and Attention-based bidirectional LSTM model achieved an accuracy of 73.6{\%} which is an increment of more than 4{\%} compared to the current state-of-the-art results.",
}
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%0 Conference Proceedings
%T Deep Learning Techniques for Humor Detection in Hindi-English Code-Mixed Tweets
%A Sane, Sushmitha Reddy
%A Tripathi, Suraj
%A Sane, Koushik Reddy
%A Mamidi, Radhika
%Y Balahur, Alexandra
%Y Klinger, Roman
%Y Hoste, Veronique
%Y Strapparava, Carlo
%Y De Clercq, Orphee
%S Proceedings of the Tenth Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis
%D 2019
%8 June
%I Association for Computational Linguistics
%C Minneapolis, USA
%F sane-etal-2019-deep
%X We propose bilingual word embeddings based on word2vec and fastText models (CBOW and Skip-gram) to address the problem of Humor detection in Hindi-English code-mixed tweets in combination with deep learning architectures. We focus on deep learning approaches which are not widely used on code-mixed data and analyzed their performance by experimenting with three different neural network models. We propose convolution neural network (CNN) and bidirectional long-short term memory (biLSTM) (with and without Attention) models which take the generated bilingual embeddings as input. We make use of Twitter data to create bilingual word embeddings. All our proposed architectures outperform the state-of-the-art results, and Attention-based bidirectional LSTM model achieved an accuracy of 73.6% which is an increment of more than 4% compared to the current state-of-the-art results.
%R 10.18653/v1/W19-1307
%U https://aclanthology.org/W19-1307
%U https://doi.org/10.18653/v1/W19-1307
%P 57-61
Markdown (Informal)
[Deep Learning Techniques for Humor Detection in Hindi-English Code-Mixed Tweets](https://aclanthology.org/W19-1307) (Sane et al., WASSA 2019)
ACL