Asian Languages Parallel Corpora: availability issues and the need for human-in-the-loop

Planning to gather or build parallel corpora for an array of languages and dialects such as Traditional Chinese, Hong Kong Chinese, Taiwanese Chinese, Thai, Burmese, Laotian, and Vietnamese is a task that is fraught with numerous challenges. Scarcity of resources combined with the linguistic and cultural nuances unique to these languages presents a substantial barrier. If unresolved, these differences can lead to issues in the precision and efficacy of the translation process. In today’s post, we delve into the daunting task that faces researchers, academics, and industry professionals in sourcing parallel corpora for these Asian languages, and underscore the critical role Human-in-the-Loop processes play in curating invaluable bilingual corpora.

Good quality Vietnamese -Italian machine translation

The Availability Challenge

The first challenge is the lack of high-quality and easily accessible monolingual or parallel corpora in these languages to build upon. This is partly due to the lower amount of content generated in these languages compared to other more predominant languages on the web, such as English, Spanish or French. In practice, this lack of parallel corpora can result in the building of lesser quality machine translation systems and lower user experience for speakers of those languages and other users of the system from larger, better resourced languages.

Additionally, the writing systems of these languages may pose another significant challenge. For the variants of Traditional Chinese, Hong Kong Chinese and Taiwanese Chinese, we are talking about languages and dialectal varieties with complex writing systems, which can make it very difficult to accurately map translations. Thai and Vietnamese also have specific features that pose challenges for machine translation systems. For example, Thai has no spaces between words, making segmentation a significant challenge.

So it’s essential that we rely on human curators to create parallel corpuses for these languages. With their knowledge of the target language and its nuances, they can ensure that the translations generated are accurate both in terms of linguistic accuracy and cultural context. It is not just a question of providing a technically correct translation, but of taking into account cultural differences which play an equally important role in communication.

Given the increasing adoption of machine translation systems in commercial and institutional contexts, translation quality can have significant impacts. An inaccurate or culturally inappropriate translation can not only lead to misunderstandings but can also undermine trust in such systems.

Crafting top-notch parallel corpora for South-East Asian and other Asian languages is an endeavor that demands a blend of tech-savviness, language proficiency, and cultural comprehension and we at NLPC excel at recruiting linguistic and NLP talent to work together on such projects. A professionally and technically well-managed human-in-the-loop process plays a pivotal role.

Our talent has meticulously sifted through and refined data to be used to train and fine-tune machine translation systems, simultaneously managing terminology and accuracy.

The value of human-in-the-loop to build off-the-shelf parallel corpora in this languages is unequivocal. As our dependence on machine translation expands across various sectors, it becomes crucial to guarantee that the machine learning these technologies are based on delivers precise results and acknowledges the cultural nuances, hues and diversity they aim to reflect.

Challenges in obtaining an Asian parallel corpus

1. Language diversity
The most immediate challenge is linguistic diversity within the regions themselves. For example, in Chinese translation services, there is a significant difference between Traditional Chinese, Hong Kong Chinese and Taiwanese Chinese. These variants present differences in vocabulary, syntax and even semantics, all of which must be taken into account when creating and using parallel corpuses.
2. Limited availability
Asian languages, particularly Thai and Vietnamese, lack substantial and freely accessible parallel corpuses, unlike languages such as English, French or Spanish. This shortage is a major obstacle for machine translation services that strive to offer complete linguistic coverage.
3. Sensitivity to context
Many Asian languages are highly context-sensitive, so the meaning of a word can change considerably depending on the context in which it is used. This complexity adds another layer of difficulty to the compilation and use of parallel corpuses for these languages.
The essential role of human selection
Given the complexities described above, human selection becomes an indispensable part of creating optimal machine translation systems for these Asian languages.

Relevant steps to build high-quality Asian corpora

1. Quality control provided by…
Human reviewers can ensure the quality of the parallel corpus by checking that the alignment is accurate and that the translations fit the context. This process is crucial to maintaining the integrity of the data from which machine translation systems learn.
2. Management of linguistic aspects:
The subtleties of language, especially in context-sensitive languages such as Thai and Vietnamese, often require human understanding to manage them effectively. Humans can discern subtle changes in meaning and tone that current AI systems can miss.
3. Culturally relevant
A crucial aspect of translation that is often overlooked is cultural relevance. Translations must not only be linguistically accurate, but also culturally sensitive and appropriate. Humans, with their understanding of cultural nuances, play a vital role in ensuring this.
4. Increased data
Human-in-the-loop processes can also augment existing parallel corpora by generating new translations, especially in domains where the available data is limited.

The Race for Better MT

Despite the challenges, there have been promising developments in recent years. For example, notable progress has been made in improving machine translation systems in Asian languages, including the development of Chinese translation services specific to different dialects. Post-editing and back-translation have been probably used in certain percentages in most systems.

The race for data also has implications for the way data is collected and used to train machine translation systems. The need for high-quality and culturally appropriate data is increasingly evident. We are likely to see a greater focus on obtaining data in a way that respects and reflects the cultures and linguistic particularities of the communities served by these systems.

The creation of high-quality Asian languages parallel corpora is a complex task requiring a combination of technical expertise, linguistic understanding and cultural sensitivity. However, through then involvement of human-in-the-loop processes, expert computational linguists and meticulous care of the source data and processing, we at NLPC ensure that Asian languages data for machine translation systems are not only technically correct, but also culturally appropriate.

Machine translation has come a long way, but there is still much to be done. As we move forward, care and attention to the needs and particularities of the world’s diverse languages and cultures will continue to be of paramount importance. Only through this human-centered approach can we achieve machine translation systems that are truly global and inclusive.

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