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- Abdulmumin, Idris, et al.
(författare)
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Separating Grains from the Chaff : Using Data Filtering to Improve Multilingual Translation for Low-Resourced African Languages
- 2022
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Ingår i: Proceedings of the Seventh Conference on Machine Translation (WMT). - : Association for Computational Linguistics. - 9781959429296 ; , s. 1001-1014
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Konferensbidrag (refereegranskat)abstract
- 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.
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| 2. |
- Muhammad, Shamsuddeen Hassan, et al.
(författare)
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SemEval-2023 Task 12 : Sentiment Analysis for African Languages (AfriSenti-SemEval)
- 2023
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Ingår i: Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023). - : Association for Computational Linguistics. - 9781959429999 ; , s. 2319-2337
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Konferensbidrag (refereegranskat)abstract
- We present the first Africentric SemEval Shared task, Sentiment Analysis for African Languages (AfriSenti-SemEval) - The dataset is available at https://github.com/afrisenti-semeval/afrisent-semeval-2023. AfriSenti-SemEval is a sentiment classification challenge in 14 African languages: Amharic, Algerian Arabic, Hausa, Igbo, Kinyarwanda, Moroccan Arabic, Mozambican Portuguese, Nigerian Pidgin, Oromo, Swahili, Tigrinya, Twi, Xitsonga, and Yorb (Muhammad et al., 2023), using data labeled with 3 sentiment classes. We present three subtasks: (1) Task A: monolingual classification, which received 44 submissions; (2) Task B: multilingual classification, which received 32 submissions; and (3) Task C: zero-shot classification, which received 34 submissions. The best performance for tasks A and B was achieved by NLNDE team with 71.31 and 75.06 weighted F1, respectively. UCAS-IIE-NLP achieved the best average score for task C with 58.15 weighted F1. We describe the various approaches adopted by the top 10 systems and their approaches.
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