Low-Resource Machine Translation Training Curriculum Fit for Low-Resource Languages

Kuwanto, Garry; Akyürek, Afra Feyza; Tourni, Isidora Chara; Li, Siyang; Jones, Alexander; Wijaya, Derry

doi:10.48550/arxiv.2103.13272

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“…CS data augmentation has been less investigated for MT. Previous work has mainly involved lexical replacements (Menacer et al, 2019;Song et al, 2019;Appicharla et al, 2021;Xu and Yvon, 2021) and back translation (Kuwanto et al, 2021b). In this section, we discuss previous work that we find closest to ours.…”

Section: Related Workmentioning

confidence: 69%

Proceedings of the The Sixth Workshop on Technologies for Machine Translation of Low-Resource Languages (LoResMT 2023)

2023

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In the past few years, machine translation (MT) performance has improved significantly. With the development of new techniques such as multilingual translation and transfer learning, the use of MT is no longer a privilege for users of popular languages. However, the goal of expanding MT coverage to more diverse languages is hindered by the fact that MT methods require large amounts of data to train quality systems. This has made developing MT systems for low-resource languages challenging. Therefore, the need for developing comparable MT systems with relatively small datasets remains highly desirable.Despite the advancements in MT technologies, creating an MT system for a new language or enhancing an existing one still requires a significant amount of effort to gather the necessary resources. The dataintensive nature of neural machine translation (NMT) approaches necessitates parallel and monolingual corpora in various domains, which are always in high demand. Developing MT systems also require dependable evaluation benchmarks and test sets. Furthermore, MT systems rely on numerous natural language processing (NLP) tools to pre-process human-generated texts into the required input format and post-process MT output into the appropriate textual forms in the target language. These tools include word tokenizers/de-tokenizers, word segmenters, and morphological analyzers, among others. The quality of these tools significantly impacts the translation output, yet there is a limited discourse on their methods, their role in training different MT systems, and their support coverage in different languages.LoResMT is a platform that aims to facilitate discussions among researchers who are working on machine translation (MT) systems and methods for low-resource, under-represented, ethnic, and endangered languages. The goal of the platform is to address the challenges associated with the development of MT systems for languages that have limited resources or are at risk of being lost. This year, LoResMT received research papers covering a wide range of languages spoken around the world. In addition to research papers, the workshop also accepts relevant findings papers at EACL 2023 to be presented at LoResMT. Aside from the research papers, LoResMT also featured two invited talks. These talks allowed participants to hear from experts in the field of MT and learn about the latest developments and challenges in MT for low-resource languages.The program committee members play a crucial role in ensuring the success of the workshop. They review the submissions and provide constructive feedback to help the authors refine their papers and ensure they meet the set standards. Without their dedication, expertise, and hard work, the workshop would not be possible. The authors who submitted their work to LoResMT are also an integral part of the workshop's success. Their research and contributions offer new insights into the field of machine translation for low-resource languages, and their participation enriches the discussions and fosters...

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Section: Related Workmentioning

confidence: 69%

Proceedings of the The Sixth Workshop on Technologies for Machine Translation of Low-Resource Languages (LoResMT 2023)

2023

View full text Add to dashboard Cite

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Low-Resource Machine Translation Training Curriculum Fit for Low-Resource Languages

Cited by 1 publication

References 0 publications

Proceedings of the The Sixth Workshop on Technologies for Machine Translation of Low-Resource Languages (LoResMT 2023)

Proceedings of the The Sixth Workshop on Technologies for Machine Translation of Low-Resource Languages (LoResMT 2023)

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