cs.CLMar 26, 2026

Toward domain-specific machine translation and quality estimation systems

Authors: Javad Pourmostafa Roshan Sharami

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Abstract

Machine Translation (MT) and Quality Estimation (QE) perform well in general domains but degrade under domain mismatch. This dissertation studies how to adapt MT and QE systems to specialized domains through a set of data-focused contributions. Chapter 2 presents a similarity-based data selection method for MT. Small, targeted in-domain subsets outperform much larger generic datasets and reach strong translation quality at lower computational cost. Chapter 3 introduces a staged QE training pipeline that combines domain adaptation with lightweight data augmentation. The method improves performance across domains, languages, and resource settings, including zero-shot and cross-lingual cases. Chapter 4 studies the role of subword tokenization and vocabulary in fine-tuning. Aligned tokenization-vocabulary setups lead to stable training and better translation quality, while mismatched configurations reduce performance. Chapter 5 proposes a QE-guided in-context learning method for large language models. QE models select examples that improve translation quality without parameter updates and outperform standard retrieval methods. The approach also supports a reference-free setup, reducing reliance on a single reference set. These results show that domain adaptation depends on data selection, representation, and efficient adaptation strategies. The dissertation provides methods for building MT and QE systems that perform reliably in domain-specific settings.

Figures & tables

AcronymDefinition
MTmachine translation
SMTstatistical machine translation
QEquality estimation
NMTneural machine translation
LLMlarge language model
PLMpre-trained language model
Table 1
Figure 1: An overview of the proposed methodology. The notation 1..n1..n in the figure indicates that the algorithm can select between 11 and nn sentences, where nn is an arbitrary number. For example, if n=5n=5 , the algorithm selects up to 55 parallel instances from the out-of-domain corpus for each in-domain sentence.
Figure 2: An iteration of selecting in-domain data
Figure 3: The difference of selected sub-corpora to in-domain test sets
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