mlsanityData to 14 Oct 2026

Utilize Transformers for translating Wikipedia category names

arXiv 2408.06124 ↗First posted 12 Aug 2024cs.CLcited by 0

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Abstract (arXiv)

On Wikipedia, articles are categorized to aid readers in navigating content efficiently. The manual creation of new categories can be laborious and time-intensive. To tackle this issue, we built language models to translate Wikipedia categories from English to Vietnamese with a dataset containing 15,000 English-Vietnamese category pairs. Subsequently, small to medium-scale Transformer pre-trained models with a sequence-to-sequence architecture were fine-tuned for category translation. The experiments revealed that OPUS-MT-en-vi surpassed other models, attaining the highest performance with a BLEU score of 0.73, despite its smaller model storage. We expect our paper to be an alternative solution for translation tasks with limited computer resources.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 10 of its 19 references that are in the map. First posted on arXiv 12 Aug 2024.

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References · 10

  1. 2017Attention Is All You Needcited by 60,361
  2. 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,599
  3. 2019BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehensioncited by 5,893
  4. 2014Neural Machine Translation by Jointly Learning to Align and Translatecited by 4,548
  5. 2014Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translationcited by 4,057
  6. 2014Sequence to Sequence Learning with Neural Networkscited by 2,986
  7. 2018A Call for Clarity in Reporting BLEU Scorescited by 1,799
  8. 2020Beyond English-Centric Multilingual Machine Translationcited by 561
  9. 2018Machine Translation: A Literature Reviewcited by 5
  10. 2022MTet: Multi-domain Translation for English and Vietnamesecited by 5

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