gaHealth: English-Irish Bilingual Health Data Corpus

Khái niệm cốt lõi

The author developed the gaHealth corpus to address the lack of parallel data for low-resource languages in the health domain, showcasing a significant improvement in translation models.

Tóm tắt

The gaHealth corpus was created to enhance Machine Translation models for low-resource languages, focusing on health data. By utilizing professionally translated documents and Covid-related data, the corpus demonstrated a 40% increase in BLEU score compared to other models. The development process involved extracting, cleaning, and aligning bilingual text files from various sources. The toolchain used for development ensured high-quality corpora by normalizing characters, detecting languages, and aligning sentences accurately. Guidelines were established to streamline the conversion process of PDF documents into a sentence-aligned corpus. The Transformer architecture was employed with optimized hyperparameters to train models for English-Irish and Irish-English translation in the health domain. Automated metrics like BLEU, TER, and ChrF were used to evaluate translation quality, showing promising results with gaHealth models outperforming other systems significantly.

Thống kê

Models using gaHealth showed a maximum BLEU score improvement of 40%.
The ga2en model achieved a BLEU score of 57.6.
The en2ga* system reached a BLEU score of 37.6.

Trích dẫn

"Developing smaller in-domain datasets can yield significant benefits overlooked by generic translation approaches."
"Our study outlines the process used in developing the corpus and empirically demonstrates the benefits of using an in-domain dataset for health data."
"The availability of large amounts of textual data is fundamental to NLP applications' success."

Thông tin chi tiết chính được chắt lọc từ

gaHealth

by Séam... lúc arxiv.org 03-07-2024

https://arxiv.org/pdf/2403.03575.pdf

Yêu cầu sâu hơn

How can the findings from developing gaHealth be applied to other low-resource language pairs

The findings from developing gaHealth can be applied to other low-resource language pairs by serving as a blueprint for creating in-domain datasets. The process outlined in the study, including data selection, pre-processing techniques, and model training with optimal hyperparameters, can be replicated for other languages facing similar resource constraints. By focusing on specific domains like health and following the linguistic guidelines established during the development of gaHealth, researchers working on low-resource language pairs can enhance translation models' performance significantly. Additionally, the approach of amalgamating multiple sources of professionally translated documents and incorporating publicly available bilingual content can be adapted to suit different languages and domains.

What are potential challenges faced when expanding the gaHealth corpus to include more Irish language documents

Expanding the gaHealth corpus to include more Irish language documents may present several challenges. One potential challenge is ensuring the quality and accuracy of translations when incorporating new sources into the dataset. Maintaining consistency in terminology across different documents from varied sources could also pose difficulties. Moreover, handling diverse document formats such as PDFs or Word files might require additional preprocessing steps tailored to each format. Another challenge could arise from aligning sentences accurately between English and Irish texts when integrating new data sources into the corpus. Managing overlaps or discrepancies between existing data and newly added content would also need careful consideration to avoid duplication or inconsistencies within the corpus.

How might incorporating additional domains like Education and Finance impact future iterations of gaHealth

Incorporating additional domains like Education and Finance into future iterations of gaHealth could have several impacts on the corpus's utility and versatility. Expanding into these domains would diversify the types of text available for translation models' training, leading to more robust systems capable of handling various subject matters effectively. Including Education-related content could improve translations related to academic materials or instructional resources, catering to a broader range of users seeking educational information in Irish.
Similarly, integrating Finance-related documents would enable better translation capabilities for financial reports, banking information, or economic analyses in both directions (EN-GA & GA-EN). This expansion would make gaHealth more comprehensive as a bilingual corpus covering multiple crucial sectors beyond just health.
Furthermore, incorporating additional domains may necessitate adjustments in preprocessing methods or linguistic guidelines specific to those fields while maintaining alignment with existing practices established during gaHealth's development process