Multilingual Text Classification for Dravidian Languages

• URL: http://arxiv.org/abs/2112.01705v1
• Date: Fri, 3 Dec 2021 04:26:49 GMT
• Title: Multilingual Text Classification for Dravidian Languages
• Authors: Xiaotian Lin, Nankai Lin, Kanoksak Wattanachote, Shengyi Jiang, Lianxi Wang
• Abstract summary: We propose a multilingual text classification framework for the Dravidian languages. On the one hand, the framework used the LaBSE pre-trained model as the base model. On the other hand, in view of the problem that the model cannot well recognize and utilize the correlation among languages, we further proposed a language-specific representation module.
• Score: 4.264592074410622
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: As the fourth largest language family in the world, the Dravidian languages have become a research hotspot in natural language processing (NLP). Although the Dravidian languages contain a large number of languages, there are relatively few public available resources. Besides, text classification task, as a basic task of natural language processing, how to combine it to multiple languages in the Dravidian languages, is still a major difficulty in Dravidian Natural Language Processing. Hence, to address these problems, we proposed a multilingual text classification framework for the Dravidian languages. On the one hand, the framework used the LaBSE pre-trained model as the base model. Aiming at the problem of text information bias in multi-task learning, we propose to use the MLM strategy to select language-specific words, and used adversarial training to perturb them. On the other hand, in view of the problem that the model cannot well recognize and utilize the correlation among languages, we further proposed a language-specific representation module to enrich semantic information for the model. The experimental results demonstrated that the framework we proposed has a significant performance in multilingual text classification tasks with each strategy achieving certain improvements.

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