<p>I've found this article that seems to answer my question:
<a href="https://hazelcast.com/glossary/machine-learning-inference/" rel="nofollow noreferrer">https://hazelcast.com/glossary/machine-learning-inference/</a></p>
<p>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.</p>
<p>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.</p>

CC BY-SA 4.0
2021-11-27T20:13:32.217Z
2021-11-27T20:13:32.217Z
ticiarai
<p>I'm reading <a href="https://aclanthology.org/2020.wat-1.21.pdf" rel="nofollow noreferrer">this paper</a> for sub-character decomposition for logographic languages and the authors mention decomposition at inference-time. They're using Transformer architecture.</p>
<p>More specifically, the authors write:</p>
<blockquote>
<p>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.</p>
</blockquote>
<p>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.</p>
<p>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.</p>

<natural-language-processing><terminology><transformer><machine-translation><inference>
What does it mean to apply decomposition at inference-time in a machine translation system?
ticiarai
# What does it mean to apply decomposition at inference-time in a machine translation system?

(Asked by: username_0 on 2021-11-23)

I'm reading [this paper](https://aclanthology.org/2020.wat-1.21.pdf) 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.

**Tags:** natural-language-processing, terminology, transformer, machine-translation, inference


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## 1 answers

### Answer by username_0 on 2021-11-27. Score: 0 | Accepted answer

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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