Papers by Abhisek Chakrabarty
FeatureBART: Feature Based Sequence-to-Sequence Pre-Training for Low-Resource NMT (2022.coling-1)
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| Challenge: | FeatureBART is a linguistically motivated sequence-to-sequence monolingual pre-training strategy . syntactic features such as lemma, part-of-speech and dependency labels are incorporated into the pre-trained model . |
| Approach: | They propose a linguistically motivated sequence-to-sequence monolingual pre-training strategy that incorporates syntactic features into the framework. |
| Outcome: | The proposed model improves translation quality in bilingual and multilingual settings over models that do not use features. |
Improving Low-Resource NMT through Relevance Based Linguistic Features Incorporation (2020.coling-main)
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| Challenge: | Existing studies on incorporating arbitrary syntactic information into neural machine translation (NMT) are lacking. |
| Approach: | They propose to integrate linguistic knowledge at different levels into neural machine translation framework to improve translation quality for language pairs with extremely limited data. |
| Outcome: | The proposed methods improve translation quality for all tasks by 3.09 BLEU points . the proposed methods are based on two different approaches . |
NGLUEni: Benchmarking and Adapting Pretrained Language Models for Nguni Languages (2024.lrec-main)
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| Challenge: | Nguni languages have over 20 million home language speakers in South Africa . there has been considerable growth in the datasets for these languages, but no analysis of the performance of NLP models for these language has been reported across languages and tasks. |
| Approach: | They compile publicly available datasets for natural language understanding and generation, spanning 6 tasks and 11 datasets. |
| Outcome: | The proposed models outperform existing models and large-scale adapted models on cross-lingual transfer and machine translation. |