Transfer learning has recently become the dominant paradigm of machine learning. Pre-
trained models fine-tuned for downstream tasks achieve better performance with fewer …

Pretrained language models (PLMs) are trained on massive corpora, but often need to
specialize to specific domains. A parameter-efficient adaptation method suggests training an …

Recent advances in NLP are brought by a range of large-scale pretrained language models
(PLMs). These PLMs have brought significant performance gains for a range of NLP tasks …

Multilingual sequence-to-sequence models perform poorly with increased language
coverage and fail to consistently generate text in the correct target language in few-shot …

Generalization and robustness to input variation are core desiderata of machine learning
research. Language varies along several axes, most importantly, language instance (eg …

Multilingual neural machine translation models (MNMT) yield state-of-the-art performance
when evaluated on data from a domain and language pair seen at training time. However …

We exploit the pre-trained seq2seq model mBART for multilingual text style transfer. Using
machine translated data as well as gold aligned English sentences yields state-of-the-art …

Metadata attributes (eg, user and product IDs from reviews) can be incorporated as
additional inputs to neural-based NLP models, by expanding the architecture of the models …

Generalising to unseen domains is under-explored and remains a challenge in neural
machine translation. Inspired by recent research in parameter-efficient transfer learning from …

The inherent complexity and uncertainty of Machine Learning (ML) makes it difficult for ML-
based Computer Vision (CV) approaches to become prevalent in safety-critical domains like …