Prompting Large Language Models with Human Error Markings for Self-Correcting Machine Translation

• URL: http://arxiv.org/abs/2406.02267v1
• Date: Tue, 4 Jun 2024 12:43:47 GMT
• Title: Prompting Large Language Models with Human Error Markings for Self-Correcting Machine Translation
• Authors: Nathaniel Berger, Stefan Riezler, Miriam Exel, Matthias Huck,
• Abstract summary: Post-editing (PE) is still required to correct errors and to enhance term translation quality in specialized domains. We present a pilot study of enhancing translation memories (TM) for the needs of correct and consistent term translation in technical domains.
• Score: 11.351365352611658
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: While large language models (LLMs) pre-trained on massive amounts of unpaired language data have reached the state-of-the-art in machine translation (MT) of general domain texts, post-editing (PE) is still required to correct errors and to enhance term translation quality in specialized domains. In this paper we present a pilot study of enhancing translation memories (TM) produced by PE (source segments, machine translations, and reference translations, henceforth called PE-TM) for the needs of correct and consistent term translation in technical domains. We investigate a light-weight two-step scenario where, at inference time, a human translator marks errors in the first translation step, and in a second step a few similar examples are extracted from the PE-TM to prompt an LLM. Our experiment shows that the additional effort of augmenting translations with human error markings guides the LLM to focus on a correction of the marked errors, yielding consistent improvements over automatic PE (APE) and MT from scratch.

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