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

Samanta, Bidisha ; Ganguly, Niloy ; Chakrabarti, Soumen (2019) Improved Sentiment Detection via Label Transfer from Monolingual to Synthetic Code-Switched Text In: 57th Annual Meeting of the Association for Computational Linguistics.

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Official URL: http://doi.org/10.18653/v1/P19-1343

Related URL: http://dx.doi.org/10.18653/v1/P19-1343

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).

Item Type:Conference or Workshop Item (Paper)
Source:Copyright of this article belongs to Association for Computational Linguistics
ID Code:130897
Deposited On:01 Dec 2022 06:18
Last Modified:27 Jan 2023 09:39

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