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Found 10 papers, 2 papers with code
A User Study of the Incremental Learning in NMT
Adaptive neural machine translation systems, able to incrementally update the underlying models under an online learning regime, have been proven to be useful to improve the efficiency of this workflow.
A Machine Translation Approach for Modernizing Historical Documents Using Backtranslation
This makes historical documents to be hard to comprehend by contemporary people and, thus, limits their accessibility to scholars specialized in the time period in which a certain document was written.
Findings of the Covid-19 MLIA Machine Translation Task
This work presents the results of the machine translation (MT) task from the Covid-19 MLIA @ Eval initiative, a community effort to improve the generation of MT systems focused on the current Covid-19 crisis.
Two Demonstrations of the Machine Translation Applications to Historical Documents
Once the user is satisfied with the system's hypothesis and validates it, the system adapts its model following an online learning strategy.
An Interactive Machine Translation Framework for Modernizing Historical Documents
Modernization aims at breaking this language barrier by generating a new version of a historical document, written in the modern version of the document's original language.
Modernizing Historical Documents: a User Study
This is due to the language barrier inherent in human language and the linguistic properties of these documents.
Incremental Adaptation of NMT for Professional Post-editors: A User Study
A common use of machine translation in the industry is providing initial translation hypotheses, which are later supervised and post-edited by a human expert.
Demonstration of a Neural Machine Translation System with Online Learning for Translators
We introduce a demonstration of our system, which implements online learning for neural machine translation in a production environment.
How Much Does Tokenization Affect Neural Machine Translation?
Separating punctuation and splitting tokens into words or subwords has proven to be helpful to reduce vocabulary and increase the number of examples of each word, improving the translation quality.
