arXiv:2601.03790v4 Announce Type: replace 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
The rapid evolution of language and the increasing sophistication of AI models are creating a demand for machine translation systems that can handle novel vocabulary effectively.
Improving machine translation's ability to understand and translate neologisms reduces communication barriers and enhances the applicability of AI in real-world, rapidly changing linguistic contexts.
Machine translation systems will become more robust and adaptable to new linguistic constructs, moving beyond static vocabularies to handle dynamic language usage more effectively.
- · Machine Translation Developers
- · Global Businesses
- · International Organizations
- · Linguistic AI Researchers
- · Monolingual Content Creators
- · Traditional MT Methods
Machine translation accuracy and utility will significantly improve, especially for informal or evolving content.
This improved accuracy will facilitate cross-cultural communication and information exchange at an accelerated pace across more languages.
The ability to rapidly translate neologisms could accelerate the global adoption of emerging concepts and trends, impacting various cultural and economic sectors.
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