In this paper, we address the problem of automatic repair of software vulnerabilities with
deep learning. The major problem with data-driven vulnerability repair is that the few …

Apparent parallels between natural language and antibody sequences have led to a surge
in deep language models applied to antibody sequences for predicting cognate antigen …

We propose a simple data augmentation protocol aimed at providing a compositional
inductive bias in conditional and unconditional sequence models. Under this protocol …

Research in NLP lacks geographic diversity, and the question of how NLP can be scaled to
low-resourced languages has not yet been adequately solved." Low-resourced"-ness is a …

We introduce a novel method for multilingual transfer that utilizes deep contextual
embeddings, pretrained in an unsupervised fashion. While contextual embeddings have …

We propose a new neural transfer method termed Dual Adversarial Transfer Network
(DATNet) for addressing low-resource Named Entity Recognition (NER). Specifically, two …

Deep learning models usually require a huge amount of data. However, these large
datasets are not always attainable. This is common in many challenging NLP tasks …