Salvato in:
| Autori principali: | , , , , , , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2025
|
| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2511.03880 |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866909889356365824 |
|---|---|
| author | Nigatu, Hellina Hailu Mamo, Bethelhem Yemane Balcha, Bontu Fufa Tesfaye, Debora Taye Zewdie, Elbethel Daniel Nesiru, Ikram Behiru Hailu, Jitu Ewnetu Yayo, Senait Mengesha |
| author_facet | Nigatu, Hellina Hailu Mamo, Bethelhem Yemane Balcha, Bontu Fufa Tesfaye, Debora Taye Zewdie, Elbethel Daniel Nesiru, Ikram Behiru Hailu, Jitu Ewnetu Yayo, Senait Mengesha |
| contents | As low-resourced languages are increasingly incorporated into NLP research, there is an emphasis on collecting large-scale datasets. But in prioritizing quantity over quality, we risk 1) building language technologies that perform poorly for these languages and 2) producing harmful content that perpetuates societal biases. In this paper, we investigate the quality of Machine Translation (MT) datasets for three low-resourced languages--Afan Oromo, Amharic, and Tigrinya, with a focus on the gender representation in the datasets. Our findings demonstrate that while training data has a large representation of political and religious domain text, benchmark datasets are focused on news, health, and sports. We also found a large skew towards the male gender--in names of persons, the grammatical gender of verbs, and in stereotypical depictions in the datasets. Further, we found harmful and toxic depictions against women, which were more prominent for the language with the largest amount of data, underscoring that quantity does not guarantee quality. We hope that our work inspires further inquiry into the datasets collected for low-resourced languages and prompts early mitigation of harmful content. WARNING: This paper contains discussion of NSFW content that some may find disturbing. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_03880 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Evaluating Machine Translation Datasets for Low-Web Data Languages: A Gendered Lens Nigatu, Hellina Hailu Mamo, Bethelhem Yemane Balcha, Bontu Fufa Tesfaye, Debora Taye Zewdie, Elbethel Daniel Nesiru, Ikram Behiru Hailu, Jitu Ewnetu Yayo, Senait Mengesha Computation and Language Computers and Society As low-resourced languages are increasingly incorporated into NLP research, there is an emphasis on collecting large-scale datasets. But in prioritizing quantity over quality, we risk 1) building language technologies that perform poorly for these languages and 2) producing harmful content that perpetuates societal biases. In this paper, we investigate the quality of Machine Translation (MT) datasets for three low-resourced languages--Afan Oromo, Amharic, and Tigrinya, with a focus on the gender representation in the datasets. Our findings demonstrate that while training data has a large representation of political and religious domain text, benchmark datasets are focused on news, health, and sports. We also found a large skew towards the male gender--in names of persons, the grammatical gender of verbs, and in stereotypical depictions in the datasets. Further, we found harmful and toxic depictions against women, which were more prominent for the language with the largest amount of data, underscoring that quantity does not guarantee quality. We hope that our work inspires further inquiry into the datasets collected for low-resourced languages and prompts early mitigation of harmful content. WARNING: This paper contains discussion of NSFW content that some may find disturbing. |
| title | Evaluating Machine Translation Datasets for Low-Web Data Languages: A Gendered Lens |
| topic | Computation and Language Computers and Society |
| url | https://arxiv.org/abs/2511.03880 |