Contextual Selection of Pseudo-terminology Constraints for Terminology-aware Neural Machine Translation in the IT Domain (2025)

Abstract: This system paper describes the development of a Neural Machine Translation system that is adapted to the Information Technology (IT) domain, and is able to translate specialized IT-related terminologies. Despite the popularity of incorporating terminology constraints at training time to develop terminology-aware Neural Machine Translation engines, one of the main issues is: In the absence of terminology references for training, and with the proliferation of source-target alignments, how does one select word alignments as pseudo-terminology constraints? The system in this work uses the encoder’s final hidden states as proxies for terminologies, and selects word alignments with the highest norm as pseudo-terminology constraints for inline annotation at run-time. It compares this context-based approach against a conventional statistical approach, where terminology-constraints are selected based on a low-frequency threshold. The systems were evaluated for general translation quality and Terminology Success Rates, with results that validate the effectiveness of the contextual approach.

## introduction this paper describes uw-benmt's submission foot_0 to wmt2025 terminology translation shared task @xcite . the aim of this edition of the shared task is to evaluate how well machine translation systems can handle specialized terms in specific domains where terminology accuracy and consistency are critical. although general machine translation systems have improved significantly, specialized terminology remains a challenge @xcite , and this task evaluates the effectiveness of integrating terminology dictionaries into machine translation en-gines in the information technology (it) domain. this is a highly practical task as machine translation engines that have been adapted to this domain can potentially assist in the international communication of technical apis, manuals and devop guides across it teams that operate in multilingual environments. this task involves segment-level terminology translation for three language pairs: @xmath0, spanish, russian}. the provided data consists of 500 parallel sentences per language pairs, with reference terminology dictionaries. ## related work there are two major approaches to terminology translation, with the first being lexical constrained decoding where the target terminologies entries are forced to match the source side lexical terms as decoding-time constraints @xcite . however, one major shortcoming of this strategy is the computational overload as the number of terminology constraints increase. to address this issue, @xcite pioneered a methodology to train neural machine translations engines to recognize terminology constraints. the focus of their approach is to annotate the data with sourcetarget terms inline as soft constraints (i.e., a form of data augmentation). the success of this approach is reflected in popularity of system submissions that adopted it at the wmt2021 and wmt2023 terminology translation shared tasks @xcite @xcite . different alternatives to implementing this approach have been proposed, such as masking out the source-side terms @xcite , which have been argued to surpass simple inline term annotations. as pointed out by @xcite , one of the main challenges of terminology transla-tion is that terminology dictionaries are not readily available for training, and their proposal differs from @xcite in that they devised a way to automatically create terminology constraints for existing training corpora. the development of pseudo-terminology constraints is a challenge in itself because it is difficult to select word alignments that are most representative of 'technical terminologies'. while @xcite and bogoychev and chen (2023) used randomly selected terms, other systems construed domain-specific terms as low frequency lexical items @xcite , a common approach that is practiced in works beyond the shared task @xcite @xcite . the main contribution of this system paper is to shed light on the context-driven approach to selecting pseudo-terminology constraints for it-adapted nmt training, and present this as an alternative to the frequency-based approach. it focuses on improving the psuedo-terminology creation process in the absence of training terminology dictionaries by comparing two methods; frequency-based, context-based. 3 selection of pseudo-terminology constraints ## evaluation systems were evaluated on the provided data, which was not used for training purposes, and have been verified to not overlap with the selected training data. although examples were taken from the data to illustrate the point in section 3.1, note that the approach in this paper does not rely on defined patterns or rules from the data, resulting in minimal risk of evaluation bias. furthermore, the provided data is the only source of gold references for quality evaluation. with these careful considerations, the provided data was safely treated as held-out test data. ## results ## conclusion this paper addresses a challenge in terminology translation engines that incorporate term annotations at run-time: with the proliferation of sourcetarget word alignments, how does one select the optimal subset of alignments as pseudo-terminology constraints? results from this work show that the encoder's hidden representations serve as useful estimations of terminology constraints. it also highlights the robustness of a main-stay data selection approach in the absence of curated it-related parallel data.