68 citations · 234 across the 28 of their papers we have counts for
6 papers · 1 filter
Distributionally Robust Recurrent Decoders with Random Network Distillation
Antonio Valerio Miceli-Barone, Alexandra Birch, Rico Sennrich
Neural machine learning models can successfully model language that is similar to their training distribution, but they are highly susceptible to degradation under distribution shi…
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…
Survey of Low-Resource Machine Translation
Barry Haddow, Rachel Bawden, Antonio Valerio Miceli Barone +2
We present a survey covering the state of the art in low-resource machine translation research. There are currently around 7000 languages spoken in the world and almost all languag…
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…