@inproceedings{miao-etal-2026-neoamt,
title = "{N}eo{AMT}: Neologism-Aware Agentic Machine Translation with Reinforcement Learning",
author = "Miao, Zhongtao and
Zhao, Kaiyan and
Nagata, Masaaki and
Tsuruoka, Yoshimasa",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.acl-long.345/",
pages = "7578--7601",
ISBN = "979-8-89176-390-6",
abstract = "Neologism-aware machine translation aims to translate source sentences containing neologisms into target languages.This field remains underexplored compared with general machine translation (MT).In this paper, we propose an agentic framework, NeoAMT, for neologism-aware machine translation equipped with a Wiktionary-based search toolkit.Specifically, we first construct a dedicated dataset for neologism-aware machine translation and build a search toolkit grounded in Wiktionary.The dataset covers 16 languages and 75 translation directions in total, derived from approximately 10 million records of an English Wiktionary dump.The retrieval corpus of the search toolkit is also constructed from around 3 million cleaned records of the same dump.We then leverage the dataset and toolkit to train a translation agent via reinforcement learning (RL) and to evaluate the accuracy of neologism-aware machine translation.Furthermore, we propose an RL training framework featuring a novel reward design and an adaptive rollout generation strategy that exploits translation difficulty to further improve the translation quality of translation agents using our search toolkit."
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<abstract>Neologism-aware machine translation aims to translate source sentences containing neologisms into target languages.This field remains underexplored compared with general machine translation (MT).In this paper, we propose an agentic framework, NeoAMT, for neologism-aware machine translation equipped with a Wiktionary-based search toolkit.Specifically, we first construct a dedicated dataset for neologism-aware machine translation and build a search toolkit grounded in Wiktionary.The dataset covers 16 languages and 75 translation directions in total, derived from approximately 10 million records of an English Wiktionary dump.The retrieval corpus of the search toolkit is also constructed from around 3 million cleaned records of the same dump.We then leverage the dataset and toolkit to train a translation agent via reinforcement learning (RL) and to evaluate the accuracy of neologism-aware machine translation.Furthermore, we propose an RL training framework featuring a novel reward design and an adaptive rollout generation strategy that exploits translation difficulty to further improve the translation quality of translation agents using our search toolkit.</abstract>
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%0 Conference Proceedings
%T NeoAMT: Neologism-Aware Agentic Machine Translation with Reinforcement Learning
%A Miao, Zhongtao
%A Zhao, Kaiyan
%A Nagata, Masaaki
%A Tsuruoka, Yoshimasa
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-390-6
%F miao-etal-2026-neoamt
%X Neologism-aware machine translation aims to translate source sentences containing neologisms into target languages.This field remains underexplored compared with general machine translation (MT).In this paper, we propose an agentic framework, NeoAMT, for neologism-aware machine translation equipped with a Wiktionary-based search toolkit.Specifically, we first construct a dedicated dataset for neologism-aware machine translation and build a search toolkit grounded in Wiktionary.The dataset covers 16 languages and 75 translation directions in total, derived from approximately 10 million records of an English Wiktionary dump.The retrieval corpus of the search toolkit is also constructed from around 3 million cleaned records of the same dump.We then leverage the dataset and toolkit to train a translation agent via reinforcement learning (RL) and to evaluate the accuracy of neologism-aware machine translation.Furthermore, we propose an RL training framework featuring a novel reward design and an adaptive rollout generation strategy that exploits translation difficulty to further improve the translation quality of translation agents using our search toolkit.
%U https://aclanthology.org/2026.acl-long.345/
%P 7578-7601
Markdown (Informal)
[NeoAMT: Neologism-Aware Agentic Machine Translation with Reinforcement Learning](https://aclanthology.org/2026.acl-long.345/) (Miao et al., ACL 2026)
ACL