Domain Terminology Integration into Machine Translation: Leveraging
  Large Language Models
Yasmin Moslem Gianfranco Romani Mahdi Molaei Rejwanul Haque John D. Kelleher Andy Way
This paper discusses the methods that we used for our submissions to the WMT
2023 Terminology Shared Task for German-to-English (DE-EN), English-to-Czech
(EN-CS), and Chinese-to-English (ZH-EN) language pairs. The task aims to
advance machine translation (MT) by challenging participants to develop systems
that accurately translate technical terms, ultimately enhancing communication
and understanding in specialised domains. To this end, we conduct experiments
that utilise large language models (LLMs) for two purposes: generating
synthetic bilingual terminology-based data, and post-editing translations
generated by an MT model through incorporating pre-approved terms. Our system
employs a four-step process: (i) using an LLM to generate bilingual synthetic
data based on the provided terminology, (ii) fine-tuning a generic
encoder-decoder MT model, with a mix of the terminology-based synthetic data
generated in the first step and a randomly sampled portion of the original
generic training data, (iii) generating translations with the fine-tuned MT
model, and (iv) finally, leveraging an LLM for terminology-constrained
automatic post-editing of the translations that do not include the required
terms. The results demonstrate the effectiveness of our proposed approach in
improving the integration of pre-approved terms into translations. The number
of terms incorporated into the translations of the blind dataset increases from
an average of 36.67% with the generic model to an average of 72.88% by the end
of the process. In other words, successful utilisation of terms nearly doubles
across the three language pairs.