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Award ID contains: 2109578

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  1. ; ; ; ; (, 9th International Conference on Language Documentation & Conservation (ICLDC))
    Language documentation involving diaspora communities presents a combination of challenges and opportunities for approaches leveraging large data collection and assisted transcription and annotation. Demonstrating our projects on Kichwa and Mapudungun, we will present a suite of our computational tools designed to effectively work with diaspora communities for language documentation. 
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    Free, publicly-accessible full text available May 3, 2026
  2. ; ; ; ; ; (, Association for Computational Linguistics)
    Free, publicly-accessible full text available November 1, 2025
  3. ; ; ; (, ELRA and ICCL)
    Kurdish, an Indo-European language spoken by over 30 million speakers, is considered a dialect continuum and known for its diversity in language varieties. Previous studies addressing language and speech technology for Kurdish handle it in a monolithic way as a macro-language, resulting in disparities for dialects and varieties for which there are few resources and tools available. In this paper, we take a step towards developing resources for language and speech technology for varieties of Central Kurdish, creating a corpus by transcribing movies and TV series as an alternative to fieldwork. Additionally, we report the performance of machine translation, automatic speech recognition, and language identification as downstream tasks evaluated on Central Kurdish subdialects. Data and models are publicly available under an open license at https://github.com/sinaahmadi/CORDI. 
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    Accepted Manuscript Full Text Available
  4. ; ; (, Association for Computational Linguistics)
    Neural machine translation (NMT) systems exhibit limited robustness in handling source-side linguistic variations. Their performance tends to degrade when faced with even slight deviations in language usage, such as different domains or variations introduced by second-language speakers. It is intuitive to extend this observation to encompass dialectal variations as well, but the work allowing the community to evaluate MT systems on this dimension is limited. To alleviate this issue, we compile and release CODET, a contrastive dialectal benchmark encompassing 891 different variations from twelve different languages. We also quantitatively demonstrate the challenges large MT models face in effectively translating dialectal variants. All the data and code have been released. 
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    Accepted Manuscript Full Text Available
  5. ; ; ; ; ; ; ; ; (, Proc. INTERSPEECH 2023)
    Full Text Available 
  6. ; (, Association for Computational Linguistics)
    Accepted Manuscript Full Text Available
  7. ; (, Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics)
    The wide accessibility of social media has provided linguistically under-represented communities with an extraordinary opportunity to create content in their native languages. This, however, comes with certain challenges in script normalization, particularly where the speakers of a language in a bilingual community rely on another script or orthography to write their native language. This paper addresses the problem of script normalization for several such languages that are mainly written in a Perso-Arabic script. Using synthetic data with various levels of noise and a transformer-based model, we demonstrate that the problem can be effectively remediated. We conduct a small-scale evaluation of real data as well. Our experiments indicate that script normalization is also beneficial to improve the performance of downstream tasks such as machine translation and language identification. 
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    Full Text Available 
  8. ; ; (, Association for Computational Linguistics)
    Full Text Available 
  9. ; (, Association for Computational Linguistics)
    Full Text Available 
  10. ; ; ; (, Association for Computational Linguistics)
    Full Text Available