Towards Code-switched Classification Exploiting Constituent Language Resources [article]

Tanvi Dadu, Kartikey Pant
<span title="2020-11-03">2020</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Code-switching is a commonly observed communicative phenomenon denoting a shift from one language to another within the same speech exchange. The analysis of code-switched data often becomes an assiduous task, owing to the limited availability of data. We propose converting code-switched data into its constituent high resource languages for exploiting both monolingual and cross-lingual settings in this work. This conversion allows us to utilize the higher resource availability for its
more &raquo; ... t languages for multiple downstream tasks. We perform experiments for two downstream tasks, sarcasm detection and hate speech detection, in the English-Hindi code-switched setting. These experiments show an increase in 22% and 42.5% in F1-score for sarcasm detection and hate speech detection, respectively, compared to the state-of-the-art.
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="">arXiv:2011.01913v1</a> <a target="_blank" rel="external noopener" href="">fatcat:jqjk2xyrvnbrhbcrcz6l7lnpf4</a> </span>
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