Improved Sentiment Detection via Label Transfer from Monolingual to Synthetic Code-Switched Text

Bidisha Samanta, Niloy Ganguly, Soumen Chakrabarti


Abstract
Multilingual writers and speakers often alternate between two languages in a single discourse. This practice is called “code-switching”. Existing sentiment detection methods are usually trained on sentiment-labeled monolingual text. Manually labeled code-switched text, especially involving minority languages, is extremely rare. Consequently, the best monolingual methods perform relatively poorly on code-switched text. We present an effective technique for synthesizing labeled code-switched text from labeled monolingual text, which is relatively readily available. The idea is to replace carefully selected subtrees of constituency parses of sentences in the resource-rich language with suitable token spans selected from automatic translations to the resource-poor language. By augmenting the scarce labeled code-switched text with plentiful synthetic labeled code-switched text, we achieve significant improvements in sentiment labeling accuracy (1.5%, 5.11% 7.20%) for three different language pairs (English-Hindi, English-Spanish and English-Bengali). The improvement is even significant in hatespeech detection whereby we achieve a 4% improvement using only synthetic code-switched data (6% with data augmentation).
Anthology ID:
P19-1343
Volume:
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
Month:
July
Year:
2019
Address:
Florence, Italy
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
3528–3537
Language:
URL:
https://www.aclweb.org/anthology/P19-1343
DOI:
10.18653/v1/P19-1343
Bib Export formats:
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PDF:
http://aclanthology.lst.uni-saarland.de/P19-1343.pdf
Video:
 https://vimeo.com/384803075