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Language modelling approaches to adaptive machine translation

Moslem, Yasmin orcid logoORCID: 0000-0003-4595-6877 (2024) Language modelling approaches to adaptive machine translation. PhD thesis, Dublin City University.

Consistency is a key requirement of high-quality translation. It is especially important to adhere to pre-approved terminology and adapt to corrected translations in domain-specific projects. Machine translation (MT) has achieved significant progress in the area of domain adaptation. However, in-domain data scarcity is common in translation settings, due to the lack of specialised datasets and terminology, or inconsistency and inaccuracy of available in-domain translations. In such scenarios where there is insufficient in-domain data to fine-tune MT models, producing translations that are consistent with the relevant context is challenging. While real-time adaptation can make use of smaller amounts of in-domain data to improve the translation on the fly, it remains challenging due to supported context limitations and efficiency constraints. Large language models (LLMs) have recently shown interesting capabilities of in-context learning, where they learn to replicate certain input-output text generation patterns, without further fine-tuning. Such capabilities have opened new horizons for domain- specific data augmentation and real-time adaptive MT. This work attempts to address two main relevant questions: 1) in scenarios involving human interaction and continuous feedback, can we employ language models to improve the quality of adaptive MT at inference time? and 2) in the absence of sufficient in-domain data, can we use pre-trained large-scale language models to improve the process of MT domain adaptation?
Item Type:Thesis (PhD)
Date of Award:March 2024
Supervisor(s):Way, Andy, Haque, Rejwanul and Kelleher, John
Uncontrolled Keywords:Terminology
Subjects:Computer Science > Artificial intelligence
Computer Science > Computational linguistics
Computer Science > Computer engineering
Computer Science > Information technology
Computer Science > Machine learning
Computer Science > Machine translating
Humanities > Language
Humanities > Linguistics
Humanities > Translating and interpreting
DCU Faculties and Centres:DCU Faculties and Schools > Faculty of Engineering and Computing > School of Computing
Research Institutes and Centres > ADAPT
Research Institutes and Centres > d-real
Use License:This item is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 License. View License
Funders:Science Foundation Ireland, Microsoft Research
ID Code:29353
Deposited On:22 Mar 2024 13:40 by Andrew Way . Last Modified 22 Mar 2024 13:40

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