gaHealth: An English–Irish Bilingual Corpus of Health Data
To assess the merits of using in-domain data, a dataset for the specific domain of health was developed for the low-resource English to Irish language pair. Our study outlines the process used in developing the corpus and empirically demonstrates the benefits of using an in-domain dataset for the health domain. In the context of translating health-related data, models developed using the gaHealth corpus demonstrated a maximum BLEU score improvement of 22.2 points (40%) when compared with top performing models from the LoResMT2021 Shared Task.
The following training files may be used for developing in-domain health models:
-- gaHealth.en.txt
-- gaHealth.ga.txt
The following files have augmented the gaHealth corpus with 8k lines of additional data from LoResMT2021:
-- gaHealth*.en.txt
-- gaHealth*.ga.txt
In order to test the accuracy of in-domain health models developed using gaHealth, the validation and test sets from LoResMT2021 were used:
-- dev.en
-- dev.ga
-- test.en.txt
-- test.ga.txt
** Corpora Stats **
Lines | Words | Characters
|
249 4278 26665 test.ga.txt
249 3394 19891 test.en.txt
502 9406 63505 dev.ga
502 7731 51525 dev.en
24338 424127 2851986 gaHealth*.ga.txt
24338 372976 2378565 gaHealth*.en.txt
Training GA-EN model with combined 17k gaHealth corpus and 8k LoResMT2021 covid corpus achieving a max validation accuracy of 38.7% and perplexity of 126 after 40k steps. BLEU score: 57.6.
Download model (297MB) ...
Training EN-GA model with combined 17k gaHealth corpus and 8k LoResMT2021 covid corpus achieving a max validation accuracy of 38.3% and perplexity of 120 after 40k steps. BLEU score: 37.6.
Download model (297MB) ...
https://bit.ly/en-ga-gaHealth-plus
Training EN-GA model with 8k LoResMT2021 covid corpus achieving a max validation accuracy of 30.0% and perplexity of 354 after 30k steps. BLEU score: 36.0.
Download model (292MB) ...
Lankford, S., Afli, H. and Way, A., 2021, August. Machine Translation in the Covid domain: an English-Irish case study for LoResMT 2021. In Proceedings of the 4th Workshop on Technologies for MT of Low Resource Languages (LoResMT2021) (pp. 144-150).
Ojha, A.K., Liu, C.H., Kann, K., Ortega, J., Shatam, S. and Fransen, T., 2021. Findings of the LoResMT 2021 Shared Task on COVID and Sign Language for Low-resource Languages. arXiv preprint arXiv:2108.06598.