Papers by Nick Thieberger
Tulun: Transparent and Adaptable Low-resource Machine Translation (2025.acl-demo)
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| Challenge: | a low-resource language that is the lingua franca in Timor-Leste lacks available corpora in the health domain. |
| Approach: | They propose a solution that combines neural MT with large language model-based post-editing guided by existing glossaries and translation memories. |
| Outcome: | The proposed system outperforms both standalone MT and LLM approaches across six low-resource languages on the FLORES dataset. |
English-based acoustic models perform well in the forced alignment of two English-based Pacific Creoles (2025.acl-long)
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Sam Passmore, Lila San Roque, Kirsty Gillespie, Saurabh Nath, Kira Davey, Keira Mullan, Tim Cawley, Jennifer Biggs, Rosey Billington, Bethwyn Evans, Nick Thieberger, Danielle Barth
| Challenge: | Currently, European languages dominate phonetic research . forced alignment can accelerate the study of sociophonetic variation in minority languages . |
| Approach: | They propose to use English and custom-made acoustic models to study the alignment of vowels in two Pacific Creoles, Tok Pisin and Bislama. |
| Outcome: | The proposed models perform acceptablely well in English and humans in vowel environments described as ‘Highly Reliable’. |