Separating Grains from the Chaff: Using Data Filtering to Improve Multilingual Translation for Low-Resourced African Languages

• URL: http://arxiv.org/abs/2210.10692v2
• Date: Thu, 20 Oct 2022 14:18:49 GMT
• Title: Separating Grains from the Chaff: Using Data Filtering to Improve Multilingual Translation for Low-Resourced African Languages
• Authors: Idris Abdulmumin, Michael Beukman, Jesujoba O. Alabi, Chris Emezue, Everlyn Asiko, Tosin Adewumi, Shamsuddeen Hassan Muhammad, Mofetoluwa Adeyemi, Oreen Yousuf, Sahib Singh, Tajuddeen Rabiu Gwadabe
• Abstract summary: This work describes our approach, which is based on filtering the given noisy data using a sentence-pair classifier. We empirically validate our approach by evaluating on two common datasets and show that data filtering generally improves overall translation quality.
• Score: 0.6947064688250465
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We participated in the WMT 2022 Large-Scale Machine Translation Evaluation for the African Languages Shared Task. This work describes 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.

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