23 citations · 52 across the 9 of their papers we have counts for
19 papers · 1 filter
Non-Autoregressive Machine Translation: It's Not as Fast as it Seems
Jindřich Helcl, Barry Haddow, Alexandra Birch
Efficient machine translation models are commercially important as they can increase inference speeds, and reduce costs and carbon emissions. Recently, there has been much interest…
Cross-lingual Intermediate Fine-tuning improves Dialogue State Tracking
Nikita Moghe, Mark Steedman, Alexandra Birch
Recent progress in task-oriented neural dialogue systems is largely focused on a handful of languages, as annotation of training data is tedious and expensive. Machine translation…
CoPHE: A Count-Preserving Hierarchical Evaluation Metric in Large-Scale Multi-Label Text Classification
Matúš Falis, Hang Dong, Alexandra Birch +1
Large-Scale Multi-Label Text Classification (LMTC) includes tasks with hierarchical label spaces, such as automatic assignment of ICD-9 codes to discharge summaries. Performance of…
Exploring Unsupervised Pretraining Objectives for Machine Translation
Christos Baziotis, Ivan Titov, Alexandra Birch +1
Unsupervised cross-lingual pretraining has achieved strong results in neural machine translation (NMT), by drastically reducing the need for large parallel data. Most approaches ad…
Few-shot learning through contextual data augmentation
Farid Arthaud, Rachel Bawden, Alexandra Birch
Machine translation (MT) models used in industries with constantly changing topics, such as translation or news agencies, need to adapt to new data to maintain their performance ov…
Language Model Prior for Low-Resource Neural Machine Translation
Christos Baziotis, Barry Haddow, Alexandra Birch
The scarcity of large parallel corpora is an important obstacle for neural machine translation. A common solution is to exploit the knowledge of language models (LM) trained on abu…