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Found 27 papers, 3 papers with code
Automatic Correction of Human Translations
We show that human errors in TEC exhibit a more diverse range of errors and far fewer translation fluency errors than the MT errors in automatic post-editing datasets, suggesting the need for dedicated TEC models that are specialized to correct human errors.
Adding Interpretable Attention to Neural Translation Models Improves Word Alignment
Multi-layer models with multiple attention heads per layer provide superior translation quality compared to simpler and shallower models, but determining what source context is most relevant to each target word is more challenging as a result.
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 Comparative Study on Vocabulary Reduction for Phrase Table Smoothing
This work systematically analyzes the smoothing effect of vocabulary reduction for phrase translation models.
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.
End-to-End Neural Word Alignment Outperforms GIZA++
Although unnecessary for training neural MT models, word alignment still plays an important role in interactive applications of neural machine translation, such as annotation transfer and lexicon injection.
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.
Automatic Bilingual Markup Transfer
We describe the task of bilingual markup transfer, which involves placing markup tags from a source sentence into a fixed target translation.
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).
