A Case Study on Filtering for End-to-End Speech Translation
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arXiv
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| Auteurs principaux: | , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866909091277832192 |
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| author | Alam, Md Mahfuz Ibn Anastasopoulos, Antonios |
| author_facet | Alam, Md Mahfuz Ibn Anastasopoulos, Antonios |
| contents | It is relatively easy to mine a large parallel corpus for any machine learning task, such as speech-to-text or speech-to-speech translation. Although these mined corpora are large in volume, their quality is questionable. This work shows that the simplest filtering technique can trim down these big, noisy datasets to a more manageable, clean dataset. We also show that using this clean dataset can improve the model's performance, as in the case of the multilingual-to-English Speech Translation (ST) model, where, on average, we obtain a 4.65 BLEU score improvement. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_01945 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A Case Study on Filtering for End-to-End Speech Translation Alam, Md Mahfuz Ibn Anastasopoulos, Antonios Computation and Language It is relatively easy to mine a large parallel corpus for any machine learning task, such as speech-to-text or speech-to-speech translation. Although these mined corpora are large in volume, their quality is questionable. This work shows that the simplest filtering technique can trim down these big, noisy datasets to a more manageable, clean dataset. We also show that using this clean dataset can improve the model's performance, as in the case of the multilingual-to-English Speech Translation (ST) model, where, on average, we obtain a 4.65 BLEU score improvement. |
| title | A Case Study on Filtering for End-to-End Speech Translation |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2402.01945 |