@inproceedings{na-etal-2025-thinker,
title = "Thinker-{DDM}: Modeling Deliberation for Machine Translation with a Drift-Diffusion Process",
author = "Na, Hongbin and
Wang, Zimu and
Maimaiti, Mieradilijiang and
Chen, Tong and
Wang, Wei and
Shen, Tao and
Chen, Ling",
editor = "Kummerfeld, Jonathan K. and
Joshi, Aditya and
Dras, Mark",
booktitle = "Proceedings of the 23rd Annual Workshop of the Australasian Language Technology Association",
month = nov,
year = "2025",
address = "Sydney, Australia",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.alta-main.4/",
pages = "45--63",
ISBN = "1834-7037",
abstract = "Large language models (LLMs) have demonstrated promising potential in various downstream tasks, including machine translation. However, prior work on LLM-based machine translation has mainly focused on better utilizing training data, demonstrations, or pre-defined and universal knowledge to improve performance, with a lack of consideration of decision-making like human translators. In this paper, we incorporate Thinker with the Drift-Diffusion Model (Thinker-DDM) to address this issue. We then redefine the Drift-Diffusion process to emulate human translators' dynamic decision-making under constrained resources. We conduct extensive experiments under the high-resource, low-resource, and commonsense translation settings using the WMT22 and CommonMT datasets, in which Thinker-DDM outperforms baselines in the first two scenarios. We also perform additional analysis and evaluation on commonsense translation to illustrate the high effectiveness and efficacy of the proposed method."
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%0 Conference Proceedings
%T Thinker-DDM: Modeling Deliberation for Machine Translation with a Drift-Diffusion Process
%A Na, Hongbin
%A Wang, Zimu
%A Maimaiti, Mieradilijiang
%A Chen, Tong
%A Wang, Wei
%A Shen, Tao
%A Chen, Ling
%Y Kummerfeld, Jonathan K.
%Y Joshi, Aditya
%Y Dras, Mark
%S Proceedings of the 23rd Annual Workshop of the Australasian Language Technology Association
%D 2025
%8 November
%I Association for Computational Linguistics
%C Sydney, Australia
%@ 1834-7037
%F na-etal-2025-thinker
%X Large language models (LLMs) have demonstrated promising potential in various downstream tasks, including machine translation. However, prior work on LLM-based machine translation has mainly focused on better utilizing training data, demonstrations, or pre-defined and universal knowledge to improve performance, with a lack of consideration of decision-making like human translators. In this paper, we incorporate Thinker with the Drift-Diffusion Model (Thinker-DDM) to address this issue. We then redefine the Drift-Diffusion process to emulate human translators’ dynamic decision-making under constrained resources. We conduct extensive experiments under the high-resource, low-resource, and commonsense translation settings using the WMT22 and CommonMT datasets, in which Thinker-DDM outperforms baselines in the first two scenarios. We also perform additional analysis and evaluation on commonsense translation to illustrate the high effectiveness and efficacy of the proposed method.
%U https://aclanthology.org/2025.alta-main.4/
%P 45-63
Markdown (Informal)
[Thinker-DDM: Modeling Deliberation for Machine Translation with a Drift-Diffusion Process](https://aclanthology.org/2025.alta-main.4/) (Na et al., ALTA 2025)
ACL