On Creating an English-Thai Code-switched Machine Translation in Medical Domain
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2024
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| author | Pengpun, Parinthapat Tiankanon, Krittamate Chinkamol, Amrest Kinchagawat, Jiramet Chairuengjitjaras, Pitchaya Supholkhan, Pasit Aussavavirojekul, Pubordee Boonnag, Chiraphat Veerakanjana, Kanyakorn Phimsiri, Hirunkul Sae-jia, Boonthicha Sataudom, Nattawach Ittichaiwong, Piyalitt Limkonchotiwat, Peerat |
| author_facet | Pengpun, Parinthapat Tiankanon, Krittamate Chinkamol, Amrest Kinchagawat, Jiramet Chairuengjitjaras, Pitchaya Supholkhan, Pasit Aussavavirojekul, Pubordee Boonnag, Chiraphat Veerakanjana, Kanyakorn Phimsiri, Hirunkul Sae-jia, Boonthicha Sataudom, Nattawach Ittichaiwong, Piyalitt Limkonchotiwat, Peerat |
| contents | Machine translation (MT) in the medical domain plays a pivotal role in enhancing healthcare quality and disseminating medical knowledge. Despite advancements in English-Thai MT technology, common MT approaches often underperform in the medical field due to their inability to precisely translate medical terminologies. Our research prioritizes not merely improving translation accuracy but also maintaining medical terminology in English within the translated text through code-switched (CS) translation. We developed a method to produce CS medical translation data, fine-tuned a CS translation model with this data, and evaluated its performance against strong baselines, such as Google Neural Machine Translation (NMT) and GPT-3.5/GPT-4. Our model demonstrated competitive performance in automatic metrics and was highly favored in human preference evaluations. Our evaluation result also shows that medical professionals significantly prefer CS translations that maintain critical English terms accurately, even if it slightly compromises fluency. Our code and test set are publicly available https://github.com/preceptorai-org/NLLB_CS_EM_NLP2024. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_16221 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | On Creating an English-Thai Code-switched Machine Translation in Medical Domain Pengpun, Parinthapat Tiankanon, Krittamate Chinkamol, Amrest Kinchagawat, Jiramet Chairuengjitjaras, Pitchaya Supholkhan, Pasit Aussavavirojekul, Pubordee Boonnag, Chiraphat Veerakanjana, Kanyakorn Phimsiri, Hirunkul Sae-jia, Boonthicha Sataudom, Nattawach Ittichaiwong, Piyalitt Limkonchotiwat, Peerat Computation and Language Artificial Intelligence Machine Learning Machine translation (MT) in the medical domain plays a pivotal role in enhancing healthcare quality and disseminating medical knowledge. Despite advancements in English-Thai MT technology, common MT approaches often underperform in the medical field due to their inability to precisely translate medical terminologies. Our research prioritizes not merely improving translation accuracy but also maintaining medical terminology in English within the translated text through code-switched (CS) translation. We developed a method to produce CS medical translation data, fine-tuned a CS translation model with this data, and evaluated its performance against strong baselines, such as Google Neural Machine Translation (NMT) and GPT-3.5/GPT-4. Our model demonstrated competitive performance in automatic metrics and was highly favored in human preference evaluations. Our evaluation result also shows that medical professionals significantly prefer CS translations that maintain critical English terms accurately, even if it slightly compromises fluency. Our code and test set are publicly available https://github.com/preceptorai-org/NLLB_CS_EM_NLP2024. |
| title | On Creating an English-Thai Code-switched Machine Translation in Medical Domain |
| topic | Computation and Language Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2410.16221 |