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Found 6 papers, 5 papers with code
Domain Generalisation of NMT: Fusing Adapters with Leave-One-Domain-Out Training
Generalising to unseen domains is under-explored and remains a challenge in neural machine translation.
Can Domains Be Transferred Across Languages in Multi-Domain Multilingual Neural Machine Translation?
Previous works mostly focus on either multilingual or multi-domain aspects of neural machine translation (NMT).
Generalised Unsupervised Domain Adaptation of Neural Machine Translation with Cross-Lingual Data Selection
Once the in-domain data is detected by the classifier, the NMT model is then adapted to the new domain by jointly learning translation and domain discrimination tasks.
Effective Unsupervised Domain Adaptation with Adversarially Trained Language Models
Recent work has shown the importance of adaptation of broad-coverage contextualised embedding models on the domain of the target task of interest.
Learning How to Active Learn by Dreaming
Heuristic-based active learning (AL) methods are limited when the data distribution of the underlying learning problems vary.
Automatic Post-Editing of Machine Translation: A Neural Programmer-Interpreter Approach
Automated Post-Editing (PE) is the task of automatically correct common and repetitive errors found in machine translation (MT) output.
