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  1. ; ( , Association for Computational Linguistics)
    Free, publicly-accessible full text available November 1, 2025
  2. ; ; ; ; ; ; ; ; ; ( , ISCA)
    Free, publicly-accessible full text available September 1, 2025
  3. ; ; ; ; ; ; ; ; ; ; et al ( , Association for Computational Linguistics)
    Free, publicly-accessible full text available August 1, 2025
  4. ; ; ; ( , Association for Computational Linguistics)
    Free, publicly-accessible full text available June 1, 2025
  5. ; ; ; ( , 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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    Free, publicly-accessible full text available May 15, 2025
  6. ; ; ; ; ( , IEEE)
    Free, publicly-accessible full text available April 14, 2025
  7. ; ; ( , 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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    Free, publicly-accessible full text available March 20, 2025
  8. ; ; ; ( , Association for Computational Linguistics)
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  9. ; ( , Association for Computational Linguistics)
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  10. ; ; ; ; ; ; ( , Association for Computational Linguistics)
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