In this paper we propose a deep learning framework for sarcasm target detection in predefined sarcastic texts. Identification of sarcasm targets can help in many core natural language processing tasks such as aspect based sentiment analysis, opinion mining etc. To begin with, we perform an empirical study of the socio-linguistic features and identify those that are statistically significant in indicating sarcasm targets (p-values in the range (0.05, 0.001)). Finally, we present a deeplearning framework augmented with sociolinguistic features to detect sarcasm targets in sarcastic book-snippets and tweets. We achieve a huge improvement in the performance in terms of exact match and dice score as compared to the current state-of-the-art baseline.
In this paper we demonstrate how codeswitching patterns can be utilised to improve various downstream NLP applications. In particular, we encode different switching features to improve humour, sarcasm and hate speech detection tasks. We believe that this simple linguistic observation can also be potentially helpful in improving other similar NLP applications.
In this paper, we advance the current state-of-the-art method for debiasing monolingual word embeddings so as to generalize well in a multilingual setting. We consider different methods to quantify bias and different debiasing approaches for monolingual as well as multilingual settings. We demonstrate the significance of our bias-mitigation approach on downstream NLP applications. Our proposed methods establish the state-of-the-art performance for debiasing multilingual embeddings for three Indian languages -Hindi, Bengali, and Telugu in addition to English. We believe that our work will open up new opportunities in building unbiased downstream NLP applications that are inherently dependent on the quality of the word embeddings used.
CCS CONCEPTS• Computing methodologies → Machine translation.
In this paper we demonstrate how codeswitching patterns can be utilised to improve various downstream NLP applications. In particular, we encode different switching features to improve humour, sarcasm and hate speech detection tasks. We believe that this simple linguistic observation can also be potentially helpful in improving other similar NLP applications.
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