The Effectiveness of Intermediate-Task Training for Code-Switched Natural Language Understanding

• URL: http://arxiv.org/abs/2107.09931v1
• Date: Wed, 21 Jul 2021 08:10:59 GMT
• Title: The Effectiveness of Intermediate-Task Training for Code-Switched Natural Language Understanding
• Authors: Archiki Prasad, Mohammad Ali Rehan, Shreya Pathak, Preethi Jyothi
• Abstract summary: We propose the use of bilingual intermediate pretraining as a reliable technique to derive performance gains on three different NLP tasks using code-switched text. We achieve substantial absolute improvements of 7.87%, 20.15%, and 10.99%, on the mean accuracies and F1 scores over previous state-of-the-art systems. We show consistent performance gains on four different code-switched language-pairs (Hindi-English, Spanish-English, Tamil-English and Malayalam-English) for SA.
• Score: 15.54831836850549
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: While recent benchmarks have spurred a lot of new work on improving the generalization of pretrained multilingual language models on multilingual tasks, techniques to improve code-switched natural language understanding tasks have been far less explored. In this work, we propose the use of bilingual intermediate pretraining as a reliable technique to derive large and consistent performance gains on three different NLP tasks using code-switched text. We achieve substantial absolute improvements of 7.87%, 20.15%, and 10.99%, on the mean accuracies and F1 scores over previous state-of-the-art systems for Hindi-English Natural Language Inference (NLI), Question Answering (QA) tasks, and Spanish-English Sentiment Analysis (SA) respectively. We show consistent performance gains on four different code-switched language-pairs (Hindi-English, Spanish-English, Tamil-English and Malayalam-English) for SA. We also present a code-switched masked language modelling (MLM) pretraining technique that consistently benefits SA compared to standard MLM pretraining using real code-switched text.

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