IndicBART for Translating Code-Mixed Kannada-English Sentences into Kannada: An Encoder-Decoder Transformer Approach
Translating Kannada-English code-mixed text continues to pose a major challenge in NLP owing to limited resource availability for Kannada, a lowresource Dravidian language, and the lack of parallel datasets. Existing models struggle with the structural complexity of code-mixed data, leading to subop...
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| Veröffentlicht in: | 2025 5th International Conference on Intelligent Technologies (CONIT) S. 1 - 6 |
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20.06.2025
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| Abstract | Translating Kannada-English code-mixed text continues to pose a major challenge in NLP owing to limited resource availability for Kannada, a lowresource Dravidian language, and the lack of parallel datasets. Existing models struggle with the structural complexity of code-mixed data, leading to suboptimal performance. To address this, we experimented with a transformer-based encoder-decoder model, leveraging two variants of IndicBART, a pre-trained multilingual model. We explored IndicBART's potential for transfer and few-shot learning by fine-tuning it on two Kannada-English code-mixed datasets: one in Roman script and the other in Kannada script, both paired with Kannada translations. Through selfattention and cross-attention mechanisms, IndicBART effectively captured the semantic essence of code-mixed sentences. Our experiments showed that both variants achieved significant BLEU scores of approximately 0.807, with each outperforming the other under different scenarios. This demonstrates their potential for code-mixed translation with minimal data. These findings highlight the effectiveness of our methodologies in tackling code-mixed translation challenges, establishing a basis for continued research in low-resource language settings. |
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| AbstractList | Translating Kannada-English code-mixed text continues to pose a major challenge in NLP owing to limited resource availability for Kannada, a lowresource Dravidian language, and the lack of parallel datasets. Existing models struggle with the structural complexity of code-mixed data, leading to suboptimal performance. To address this, we experimented with a transformer-based encoder-decoder model, leveraging two variants of IndicBART, a pre-trained multilingual model. We explored IndicBART's potential for transfer and few-shot learning by fine-tuning it on two Kannada-English code-mixed datasets: one in Roman script and the other in Kannada script, both paired with Kannada translations. Through selfattention and cross-attention mechanisms, IndicBART effectively captured the semantic essence of code-mixed sentences. Our experiments showed that both variants achieved significant BLEU scores of approximately 0.807, with each outperforming the other under different scenarios. This demonstrates their potential for code-mixed translation with minimal data. These findings highlight the effectiveness of our methodologies in tackling code-mixed translation challenges, establishing a basis for continued research in low-resource language settings. |
| Author | N, Shruthi Sooda, Kavitha |
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| Snippet | Translating Kannada-English code-mixed text continues to pose a major challenge in NLP owing to limited resource availability for Kannada, a lowresource... |
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| SubjectTerms | Code-mixed texts Complexity theory Data models Encoder-Decoder Transformer Model Few shot learning IndicBart Kannada-English Code-mixed Multilingual Neural machine translation NLP Semantics Transformers Translation |
| Title | IndicBART for Translating Code-Mixed Kannada-English Sentences into Kannada: An Encoder-Decoder Transformer Approach |
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