6 citations · 23 across the 18 of their papers we have counts for
6 papers · 1 filter
Minimum Bayes' Risk Decoding for System Combination of Grammatical Error Correction Systems
Vyas Raina, Mark Gales
For sequence-to-sequence tasks it is challenging to combine individual system outputs. Further, there is also often a mismatch between the decoding criterion and the one used for a…
Sentiment Perception Adversarial Attacks on Neural Machine Translation Systems
Vyas Raina, Mark Gales
With the advent of deep learning methods, Neural Machine Translation (NMT) systems have become increasingly powerful. However, deep learning based systems are susceptible to advers…
Sample Attackability in Natural Language Adversarial Attacks
Vyas Raina, Mark Gales
Adversarial attack research in natural language processing (NLP) has made significant progress in designing powerful attack methods and defence approaches. However, few efforts hav…
CUED at ProbSum 2023: Hierarchical Ensemble of Summarization Models
Potsawee Manakul, Yassir Fathullah, Adian Liusie +3
In this paper, we consider the challenge of summarizing patients' medical progress notes in a limited data setting. For the Problem List Summarization (shared task 1A) at the BioNL…
Rewarding Chatbots for Real-World Engagement with Millions of Users
Robert Irvine, Douglas Boubert, Vyas Raina +11
The emergence of pretrained large language models has led to the deployment of a range of social chatbots for chitchat. Although these chatbots demonstrate language ability and flu…
Identifying Adversarially Attackable and Robust Samples
Vyas Raina, Mark Gales
Adversarial attacks insert small, imperceptible perturbations to input samples that cause large, undesired changes to the output of deep learning models. Despite extensive research…