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Neural Machine Translation Models Can Learn to be Few-shot Learners
The emergent ability of Large Language Models to use a small number of examples to learn to perform in novel domains and tasks, also called in-context learning (ICL).
The Impact of Text Presentation on Translator Performance
Widely used computer-aided translation (CAT) tools divide documents into segments such as sentences and arrange them in a side-by-side, spreadsheet-like view.
Measuring Immediate Adaptation Performance for Neural Machine Translation
Incremental domain adaptation, in which a system learns from the correct output for each input immediately after making its prediction for that input, can dramatically improve system performance for interactive machine translation.
Compact Personalized Models for Neural Machine Translation
We propose and compare methods for gradient-based domain adaptation of self-attentive neural machine translation models.
A User-Study on Online Adaptation of Neural Machine Translation to Human Post-Edits
The advantages of neural machine translation (NMT) have been extensively validated for offline translation of several language pairs for different domains of spoken and written language.
A Post-editing Interface for Immediate Adaptation in Statistical Machine Translation
Our translation systems may learn from post-edits using several weight, language model and novel translation model adaptation techniques, in part by exploiting the output of the graphical interface.
