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Separating Grains f...
Separating Grains from the Chaff : Using Data Filtering to Improve Multilingual Translation for Low-Resourced African Languages
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- Abdulmumin, Idris (författare)
- Ahmadu Bello University, Zaria, Nigeria; HausaNLP
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- Beukman, Michael (författare)
- University of the Witwatersrand, South Africa
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- Alabi, Jesujoba O. (författare)
- Saarland University, Germany
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- Emezue, Chris (författare)
- TUM, Germany; Mila - Quebec AI Institute
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- Asiko, Everlyn (författare)
- University of Cape Town, South Africa; African Institute for Mathematical Sciences
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- Adewumi, Oluwatosin, 1978- (författare)
- Luleå tekniska universitet,EISLAB
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- Muhammad, Shamsuddeen Hassan (författare)
- HausaNLP; LIAAD-INESC TEC, Porto, Portugal
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- Adeyemi, Mofetoluwa (författare)
- Uppsala University, Sweden
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- Yousuf, Oreen (författare)
- Uppsala University, Sweden
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- Singh, Sahib (författare)
- Ford Motor Company
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- Gwadabe, Tajuddeen Rabiu (författare)
- HausaNLP; University of Chinese Academy of Sciences, China
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(creator_code:org_t)
- Association for Computational Linguistics, 2022
- 2022
- Engelska.
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Ingår i: Proceedings of the Seventh Conference on Machine Translation (WMT). - : Association for Computational Linguistics. - 9781959429296 ; , s. 1001-1014
- Relaterad länk:
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https://urn.kb.se/re...
Abstract
Ämnesord
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- We participated in the WMT 2022 Large-Scale Machine Translation Evaluation for the African Languages Shared Task. This work de-scribes our approach, which is based on filtering the given noisy data using a sentence-pair classifier that was built by fine-tuning a pre-trained language model. To train the classifier, we obtain positive samples (i.e. high-quality parallel sentences) from a gold-standard curated dataset and extract negative samples (i.e.low-quality parallel sentences) from automatically aligned parallel data by choosing sentences with low alignment scores. Our final machine translation model was then trained on filtered data, instead of the entire noisy dataset. We empirically validate our approach by evaluating on two common datasets and show that data filtering generally improves overall translation quality, in some cases even significantly.
Ämnesord
- NATURVETENSKAP -- Data- och informationsvetenskap -- Språkteknologi (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Language Technology (hsv//eng)
Nyckelord
- Maskininlärning
- Machine Learning
Publikations- och innehållstyp
- ref (ämneskategori)
- kon (ämneskategori)
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Abdulmumin, Idri ...
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Beukman, Michael
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Alabi, Jesujoba ...
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Emezue, Chris
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Asiko, Everlyn
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Adewumi, Oluwato ...
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visa fler...
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Muhammad, Shamsu ...
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Adeyemi, Mofetol ...
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Yousuf, Oreen
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Singh, Sahib
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Gwadabe, Tajudde ...
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