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I'm reading this paper for sub-character decomposition for logographic languages and the authors mention decomposition at inference-time. They're using Transformer architecture.

More specifically, the authors write:

We propose a flexible inference-time sub-character decomposition procedure which targets unseen characters, and show that it aids adequacy and reduces misleading overtranslation in unseen character translation.

What do inference-time and inference-only decomposition mean in this context? My best guess would be that inference-time would be at some point during the decoding process, but I'm not 100% clear on whether that's the case and, if so, when exactly.

I'm going to keep digging and update if I find something helpful. In the meantime, if anyone needs more context just let me know.

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I've found this article that seems to answer my question: https://hazelcast.com/glossary/machine-learning-inference/

From this, my understanding is that inference-time describes when a machine learning system is put into use following training; so basically at the time of task application.

I think this would mean that the paper's authors are stating that the decomposition of sub-characters is occurring whenever the model is actively translating languages in a production environment.

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