Switch Point biased Self-Training: Re-purposing Pretrained Models for Code-Switching (2021.findings-emnlp)
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| Challenge: | Code-switching (CS) is a phenomenon of switching between multiple languages . current models cannot handle CS due to lack of annotated data and limited resources. |
| Approach: | They propose a self-training method to repurpose existing models using a switch-point bias by leveraging unannotated data to reduce the gap between the switch point performance and retain overall performance on two distinct language pairs. |
| Outcome: | The proposed model reduces the gap between the switch point performance while retaining the overall performance on two distinct language pairs. |
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| Challenge: | Code-switching is a prevalent linguistic phenomenon in which multilingual individuals seamlessly alternate between languages. |
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Jiandong Shao, Raphael Tang, Crystina Zhang, Karin Sevegnani, Pontus Stenetorp, Jianfei Yang, Yao Lu
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| Challenge: | Amidst the rapid advances of large language models, most LLMs struggle with mixed-language inputs, limited Code-switching datasets, and evaluation biases. |
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