# Translating Variation in Multi-Word Terms: Is the human going to stay in the loop?

Natalie Kübler

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Natalie Kübler. Translating Variation in Multi-Word Terms: Is the human going to stay in the loop?. K. Valeontis, F. Kazala, M. Panagiotou, E. Tziafa, G. Tsiamas, E. Fragkou. Proceedings of the 15th Conference "Hellenic Language and Terminology", Athènes: ELETO, pp.28-40, 2025, 978-618-5788-04-9. hal-05442436

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Submitted on 9 Jan 2026

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# Translating Variation in Multi-Word Terms: Is the human going to stay in the loop?

Natalie Kübler

## Abstract

Over the past two decades, interest in variation in terminology has steadily increased (Humbley & Picton 2017), and in the last twenty years, significant progress has been made in this field. The emergence of corpora in digital format, along with the development of corpus linguistics approaches, has made it possible to systematically observe the recurring presence of variations in texts. Moreover, terminologists, who were initially domain experts with a strong interest in languages, have progressively evolved into linguists and/or translators—specialists in language rather than experts in specific subject areas. This shift has elevated the role of corpora, which facilitate the extraction of specialised and contextual information about terms. Consequently, research has moved from controlling variation to taking it into account as a legitimate phenomenon that must be analysed to better understand it.

In linguistic practice, variation constitutes a source of uncertainty for translators and technical writers (Alarcon-Navio et al. 2016). Language professionals have to rely on terminological resources (term bases) to help them finding the correct equivalent and dealing correctly with term variation. However, terminological resources are not always (and cannot be) complete and up-to-date, especially with Multi-Word-Terms (MWT). These MWT are prone to variation for a wide range.

## Περίληψη

Τις τελευταίες δύο δεκαετίες, το ενδιαφέρον για τις παραλλαγές στην ορολογία έχει αυξηθεί σταθερά (Humbley & Picton 2017) και τα τελευταία είκοσι χρόνια έχουν σημειωθεί σημαντικές προόδους στον τομέα αυτό. Η εμφάνιση των ψηφιακών σωμάτων κειμένων, σε συνδυασμό με την ανάπτυξη των μεθόδων της γλωσσολογίας σωμάτων κειμένων, έχει καταστήσει δυνατή τη συστηματική παρατήρηση της επαναλαμβανόμενης παρουσίας παραλλαγών στα κείμενα. Επιπλέον, οι ορολόγοι, οι οποίοι αρχικά ήταν ειδικοί σε συγκεκριμένους τομείς με έντονο ενδιαφέρον για τις γλώσσες, έχουν εξελιχθεί σταδιακά σε γλωσσολόγους και/ή μεταφραστές, δηλαδή σε ειδικούς στη γλώσσα και όχι σε συγκεκριμένους θεματικούς τομείς. Αυτή η αλλαγή έχει αναδείξει τον ρόλο των σωμάτων κειμένων, τα οποία διευκολύνουν την εξαγωγή εξειδικευμένων και συμφραζομένων πληροφοριών σχετικά με τους όρους. Κατά συνέπεια, η
έρευνα έχει μετατοπιστεί από τον έλεγχο της ποικιλίας στην αναγνώρισή της ως ένα νόμιμο
φαινόμενο που πρέπει να αναλυθεί για να κατανοηθεί καλύτερα.

Στη γλωσσική πρακτική, η παραλλαγή αποτελεί πηγή αβεβαιότητας για τους μεταφραστές και
τους τεχνικούς συγγραφείς (Alarcon-Navio et al. 2016). Οι επαγγελματίες της γλώσσας πρέπει
να βασίζονται σε ορολογικούς πόρους (βάσεις όρων) για να τους βοηθήσουν να βρουν το
σωστό αντίστοιχο και να αντιμετωπίσουν σωστά τη διακύμανση των όρων. Ωστόσο, οι
ορολογικοί πόροι δεν είναι πάντα (και δεν μπορούν να είναι) πλήρεις και ενημερωμένοι, ειδικά
με τους όρους πολλαπλών λέξεων (MWT). Αυτοί οι MWT είναι επιρρεπείς σε διακύμανση σε
ένα ευρύ φάσμα.

0. Introduction

Over the past two decades, interest in variation in terminology has steadily increased (Humbley & Picton 2017), and in the last twenty years, significant progress has been made in this field. The emergence of corpora in digital format, along with the development of corpus linguistics approaches, has made it possible to systematically observe the recurring presence of variations in texts. Moreover, terminologists, who were initially domain experts with a strong interest in languages, have progressively evolved into linguists and/or translators—specialists in language rather than experts in specific subject areas. This shift has elevated the role of corpora, which facilitate the extraction of specialised and contextual information about terms. Consequently, research has moved from controlling variation to taking it into account as a legitimate phenomenon that must be analysed to better understand it.

In linguistic practice, variation constitutes a source of uncertainty for translators and technical
writers (Alarcon-Navio et al. 2016). Language professionals have to rely on terminological
resources (term bases) to help them finding the correct equivalent and dealing correctly with
term variation. However, terminological resources are not always (and cannot be) complete
and up-to-date, especially with Multi-Word-Terms (MWT). These MWT are prone to variation
for a wide range of reasons that will be detailed. An additional difficulty lies in the fact that
MWT are often part of complex noun phrases (CNPs, Kübler et al. 2018), which makes it
difficult for translators to find the correct equivalent and to process term variation. The
increasing use of machine translation tools, whether Neural Machine Translation (NMT) or generative AI, brings back these long-standing questions that the translation community has faced in relation to machines (Kübler et al. 2022, Cabezas-Garcia & Leon-Arauz, 2023). Over the last decades, much has been done to specialise machines with terminological resources (Semenov et al. 2023). However, MWT terms processing has received little attention and machines are not consistent while translating terms, especially in CNPs, thus introducing inappropriate variation.

The advent of generative AI has changed the game. AI's ability to predict words, structures, etc. makes it possible, for example, to extract terms from a text and obtain their equivalents in another language, thereby processing bilingual terminology upstream of the translation process. Thanks to prompts and Chain-of Thought Prompting, generative AI can produce excellent translations, which may, however, contain errors. Generative AI can also be used as a help to evaluate translated texts, but the answers can be prone to errors or inconsistencies.

After a reminder of term, and especially, MWT variation, we will tackle the issue of translating term variation with examples in English and French. We will then address the issue of evaluating how TAN and generative AI translate terminological variation and Multi-Word Terms, particularly within complex noun phrases. Finally, we will show some possible solutions, especially on a human-in-the loop approach of Generative AI.

## Bibliographic References

Alarcón-Navío, E., López-Rodríguez, C., & Tercedor-Sánchez, M. 2016. Variation dé nominative et familiarité en tant que source d'incertitude en traduction médicale. *Meta, 61*(1), 117-144.

Cabezas-García, M., & León-Araúz, P. (2023). Machine versus corpus-based translation of multiword terms. *Digital Scholarship in the Humanities, 38*(Supplement_1), i6-i16.

Humbley, J., & Picton, A. (2017). Introduction: Multiple perspectives on terminological variation. In *Multiple perspectives on terminological variation* (pp. 1-7). John Benjamins Publishing Company.

Kübler, N., Mestivier, A., & Pecman, M. (2022). Using comparable corpora for translating and post-editing complex noun phrases in specialized texts. *Extending the scope of corpus-based translation studies*, p-237-266.

Kübler, N., Mestivier, A., & Pecman, M. (2018). Teaching specialised translation through corpus linguistics: translation quality assessment and methodology evaluation and enhancement by experimental approach. *Meta, 63*(3), 807-825.
Semenov, K., Zouhar, V., Kocmi, T., Zhang, D., Zhou, W., & Jiang, Y. E. (2023, December). Findings of the WMT 2023 Shared Task on Machine Translation with Terminologies. In *Proceedings of the Eighth Conference on Machine Translation* (pp. 663-671).