Cascading Adaptors to Leverage English Data to Improve Performance of Question Answering for Low-Resource Languages
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arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2021
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| _version_ | 1866914932527726592 |
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| author | Pandya, Hariom A. Ardeshna, Bhavik Bhatt, Brijesh S. |
| author_facet | Pandya, Hariom A. Ardeshna, Bhavik Bhatt, Brijesh S. |
| contents | Transformer based architectures have shown notable results on many down streaming tasks including question answering. The availability of data, on the other hand, impedes obtaining legitimate performance for low-resource languages. In this paper, we investigate the applicability of pre-trained multilingual models to improve the performance of question answering in low-resource languages. We tested four combinations of language and task adapters using multilingual transformer architectures on seven languages similar to MLQA dataset. Additionally, we have also proposed zero-shot transfer learning of low-resource question answering using language and task adapters. We observed that stacking the language and the task adapters improves the multilingual transformer models' performance significantly for low-resource languages. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2112_09866 |
| institution | arXiv |
| publishDate | 2021 |
| record_format | arxiv |
| spellingShingle | Cascading Adaptors to Leverage English Data to Improve Performance of Question Answering for Low-Resource Languages Pandya, Hariom A. Ardeshna, Bhavik Bhatt, Brijesh S. Computation and Language Artificial Intelligence Human-Computer Interaction Information Retrieval Machine Learning Transformer based architectures have shown notable results on many down streaming tasks including question answering. The availability of data, on the other hand, impedes obtaining legitimate performance for low-resource languages. In this paper, we investigate the applicability of pre-trained multilingual models to improve the performance of question answering in low-resource languages. We tested four combinations of language and task adapters using multilingual transformer architectures on seven languages similar to MLQA dataset. Additionally, we have also proposed zero-shot transfer learning of low-resource question answering using language and task adapters. We observed that stacking the language and the task adapters improves the multilingual transformer models' performance significantly for low-resource languages. |
| title | Cascading Adaptors to Leverage English Data to Improve Performance of Question Answering for Low-Resource Languages |
| topic | Computation and Language Artificial Intelligence Human-Computer Interaction Information Retrieval Machine Learning |
| url | https://arxiv.org/abs/2112.09866 |