Enabling Medical Translation for Low-Resource Languages

Musleh, Ahmad; Durrani, Nadir; Temnikova, Irina; Nakov, Preslav; Vogel, Stephan; Alsaad, Osama

doi:10.1007/978-3-319-75487-1_1

Cited by 3 publications

(3 citation statements)

References 25 publications

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“…"Enabling Medical Translation for Low-Resource Languages" (Musleh et al, 2016) briefly describes some speech translation systems available at the time of writing while developing text translation for Urdu, an under-resourced language closely related to Hindi and important for healthcare in Qatar. The paper provides useful historical context, even as several of the surveyed systems remain active.…”

Section: Surveys Of Speech Translation Systemsmentioning

confidence: 99%

Speech and Translation Technologies

Seligman

2023

Translation Technology in Accessible Health Communication

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mark seligman IntroductionCross-language communication in healthcare is urgently needed. Daily and nightly throughout the world, thousands of conversations are required between caregiversdoctors, nurses, administrators, volunteers, and othersand patients or family members with differing native languages.Chapter 1 describes and illustrates the exploding development of the relevant linguistic technologiesmachine translation (MT) of text, automatic speech recognition (ASR), and text-to-speech (TTS). The related infrastructurewireless communication, cloud computing, and mobile deviceshas also been developing apace. This chapter will shift focus to the combination and application of these technologies in the healthcare context, with special interest in speech translation.Given this impressive and accelerating progress, we'd expect various automatic translation and speech-enabled systems to be in widespread use by now; in fact, however, adoption remains slow. We'll examine the obstacles to adoption and directions for overcoming them in Section 2.2. In Section 2.3, we'll examine two major types of speech translation systems, concentrating on their respective approaches to the same obstacles. Section 2.4 will survey some healthcare-oriented communication systems, past and future. We'll conclude with an optimistic forecast for speech and translation applications in healthcare, tempered by due cautions. Obstacles to Adoption and Potential SolutionsOne key factor in the lagging adoption of linguistic technology in healthcare is the sheer difficulty of understanding the relevant technologies, and thus the natural hesitation to trust them. Accordingly, Chapter 1 aimed to bridge the 47

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Section: Surveys Of Speech Translation Systemsmentioning

confidence: 99%

Speech and Translation Technologies

Seligman

2023

Translation Technology in Accessible Health Communication

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“…However, such systems are usually designed to transliterate between two closely related languages. Examples include the work of Musleh et al [38]…”

Section: Related Workmentioning

confidence: 99%

Atar: Attention-based LSTM for Arabizi transliteration

Talafha

Abuammar

Al‐Ayyoub

2021

IJECE

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A non-standard romanization of Arabic script, known as Arbizi, is widely used in Arabic online and SMS/chat communities. However, since state-of-the-art tools and applications for Arabic NLP expects Arabic to be written in Arabic script, handling contents written in Arabizi requires a special attention either by building customized tools or by transliterating them into Arabic script. The latter approach is the more common one and this work presents two significant contributions in this direction. The first one is to collect and publicly release the first large-scale “Arabizi to Arabic script” parallel corpus focusing on the Jordanian dialect and consisting of more than 25 k pairs carefully created and inspected by native speakers to ensure highest quality. Second, we present Atar, an attention-based encoder-decoder model for Arabizi transliteration. Training and testing this model on our dataset yields impressive accuracy (79%) and BLEU score (88.49).

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“…In [Irvine 2013], monolingual and comparable data sets were used to improve SMT on low resource conditions based on approaches of translating unknown words, bilingual lexicon induction, and scoring phrase tables. [Musleh et al 2016] presented a work of data collection from the web to improve SMT for low-resource language pairs. One of the most recent work of SMT for low-resource languages has been presented in , which introduced approaches to source language adaptation for resource-poor SMT using a large bi-text for a related resource-rich language and obtained significant improvement.…”

Section: Phrase-based Machine Translation On Low-resource Languagesmentioning

confidence: 99%

Leveraging Additional Resources for Improving Statistical Machine Translation on Asian Low-Resource Languages

Trieu

Tran

Ittoo

et al. 2019

ACM Trans. Asian Low-Resour. Lang. Inf. Process.

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Phrase-based machine translation (MT) systems require large bilingual corpora for training. Nevertheless, such large bilingual corpora are unavailable for most language pairs in the world, causing a bottleneck for the development of MT. For the Asian language pairs—Japanese, Indonesian, Malay paired with Vietnamese—they are also not excluded from the case, in which there are no large bilingual corpora on these low-resource language pairs. Furthermore, although the languages are widely used in the world, there is no prior work on MT, which causes an issue for the development of MT on these languages. In this article, we conducted an empirical study of leveraging additional resources to improve MT for the Asian low-resource language pairs: translation from Japanese, Indonesian, and Malay to Vietnamese. We propose an innovative approach that lies in two strategies of building bilingual corpora from comparable data and phrase pivot translation on existing bilingual corpora of the languages paired with English. Bilingual corpora were built from Wikipedia bilingual titles to enhance bilingual data for the low-resource languages. Additionally, we introduced a combined model of the additional resources to create an effective solution to improve MT on the Asian low-resource languages. Experimental results show the effectiveness of our systems with the improvement of +2 to +7 BLEU points. This work contributes to the development of MT on low-resource languages, especially opening a promising direction for the progress of MT on the Asian language pairs.

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Enabling Medical Translation for Low-Resource Languages

Cited by 3 publications

References 25 publications

Speech and Translation Technologies

Speech and Translation Technologies

Atar: Attention-based LSTM for Arabizi transliteration

Leveraging Additional Resources for Improving Statistical Machine Translation on Asian Low-Resource Languages

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