Enhancing Neural Machine Translation for A inu- J apanese: A Comprehensive Study on the Impact of Domain and Dialect Integration (2024)

Abstract: Neural Machine Translation (NMT) has revolutionized language translation, yet significant challenges persist for low-resource languages, particularly those with high dialectal variation and limited standardization. This comprehensive study focuses on the Ainu language, a critically endangered indigenous language of northern Japan, which epitomizes these challenges. We address the limitations of previous research through two primary strategies: (1) extensive corpus expansion encompassing diverse domains and dialects, and (2) development of innovative methods to incorporate dialect and domain information directly into the translation process. Our approach yielded substantial improvements in translation quality, with BLEU scores increasing from 32.90 to 39.06 (+6.16) for Japanese → Ainu and from 10.45 to 31.83 (+21.38) for Ainu → Japanese. Through rigorous experimentation and analysis, we demonstrate the crucial importance of integrating linguistic variation information in NMT systems for languages characterized by high diversity and limited resources. Our findings have broad implications for improving machine translation for other low-resource languages, potentially advancing preservation and revitalization efforts for endangered languages worldwide.

## introduction ainu is the indigenous language of the ainu people, who are native to northern japan, sakhalin, and the kuril islands. due to the japanese government's assimilation policy during the 20th century, the number of people speaking ainu as their first language drastically declined. today, unesco classifies ainu as a critically endangered language, and estimates suggest that fewer than ten native speakers remain, all of whom are elderly @xcite . however, there has been a growing focus on revitalizing the ainu language in recent years. this development follows the japanese government's official recognition of the ainu as an indigenous people, which has led to national funding for ainu language courses and educational materials @xcite . many ainu learners speak japanese as their first language today; thus, practical machine translation is integral to the revitalization of ainu. however, a previous study by @xcite faced significant challenges, including difficulties in distinguishing between different dialects and challenges in translating everyday conversation. to address these problems, we carried out the following approaches. firstly, we enhanced the corpus. previous studies' corpora were predominantly biased toward folklore from limited regions. we gathered and digitized resources from various dialects and domains to ensure greater diversity. we also introduced a novel approach to ainu-japanese translation that can distinguish dialects and domains, reducing wording confusion between different regions or contexts. in this paper, we elaborate on the details of the methodology, present our results, and discuss the implications of our findings that can potentially contribute to the revitalization of ainu, which may also apply to other low-resource languages. ## background and related work in this section, we will review the background of the ainu language and discuss previous studies. ## methodology this section details our methodology for selecting materials in the corpus, preparing them in a format suitable for machine learning, and conducting the training process. ## results in this section, we elaborate on the results of the experiments. ## discussion this section discusses how multilingual pre-trained models, domain, and dialectal variations impact ainu machine translation. ## conclusion this study demonstrates the effectiveness of incorporating dialect and domain information in nmt systems for low-resource languages with high linguistic variation. by expanding the corpus and leveraging task prefixes to provide contextual information, we achieved significant improvements in ainu-japanese translation performance. our research contributes to the broader field of low-resource language nmt by: as we continue to refine and expand these methods, we move closer to achieving effective machine translation for all languages, irrespective of their resource status. this work not only advances nmt research but also contributes to broader language preservation and revitalization efforts, providing new tools and methodologies for engaging with endangered languages.