A large English–Thai parallel corpus from the web and machine-generated text.

The primary objective of our work is to build a large-scale English–Thai dataset for training neural machine translation models. We construct scb-mt-en-th-2020, an English–Thai machine translation dataset with over 1 million segment pairs, curated from various sources: news, Wikipedia articles, SMS...

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Publicado en:Language Resources & Evaluation Vol. 56; no. 2; pp. 477 - 500
Autores principales: Lowphansirikul, Lalita, Polpanumas, Charin, Rutherford, Attapol T., Nutanong, Sarana
Formato: Artículo
Publicado: Springer Nature Jun2022
Materias:
Acceso en línea:Ver este registro en EBSCOhost
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          Lowphansirikul, Lalita
          Polpanumas, Charin
          Rutherford, Attapol T.
          Nutanong, Sarana
        affil:
          School of Information Science and Technology, Vidyasirimedhi Institution of Science and Technology, Rayong, Thailand
          PyThaiNLP, Bangkok, Thailand
          Department of Linguistics, Chulalongkorn University, Bangkok, Thailand
          Teaching and Learning Thai as a Foreign Language Group, Bangkok, Thailand
      su:
        Wikipedia
        Google Inc.
        Machine translating
        Corpora
        Government publications
        Attribution of news
        Source code
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          Wikipedia
          Google Inc.
          Machine translating
          Corpora
          Government publications
          Attribution of news
          Source code
      keyword:
        Machine translation
        Parallel corpus
        Pretraining
        Thai language
        Transformer
      ab: The primary objective of our work is to build a large-scale English–Thai dataset for training neural machine translation models. We construct scb-mt-en-th-2020, an English–Thai machine translation dataset with over 1 million segment pairs, curated from various sources: news, Wikipedia articles, SMS messages, task-based dialogs, web-crawled data, government documents, and text artificially generated by a pretrained language model. We present the methods for gathering data, aligning texts, and removing preprocessing noise and translation errors automatically. We also train machine translation models based on this dataset to assess the quality of the corpus. Our models perform comparably to Google Translation API (as of May 2020) for Thai–English and outperform Google when the Open Parallel Corpus (OPUS) is included in the training data for both Thai–English and English–Thai translation. The dataset is available for public use under CC-BY-SA 4.0 License. The pre-trained models and source code to reproduce our work are available under Apache-2.0 License.
      pubtype: Academic Journal
      doctype: Article
      src: R
    language: English
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      custom: Language Resources & Evaluation is a copyright of Springer, 2022. All Rights Reserved.
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