3 citations · 5 across the 2 of their papers we have counts for
6 papers
ECOL-R: Encouraging Copying in Novel Object Captioning with Reinforcement Learning
Yufei Wang, Ian D. Wood, Stephen Wan +1
Novel Object Captioning is a zero-shot Image Captioning task requiring describing objects not seen in the training captions, but for which information is available from external ob…
Detecting and Exorcising Statistical Demons from Language Models with Anti-Models of Negative Data
Michael L. Wick, Kate Silverstein, Jean-Baptiste Tristan +2
It's been said that "Language Models are Unsupervised Multitask Learners." Indeed, self-supervised language models trained on "positive" examples of English text generalize in desi…
End-to-End Speech Recognition and Disfluency Removal
Paria Jamshid Lou, Mark Johnson
Disfluency detection is usually an intermediate step between an automatic speech recognition (ASR) system and a downstream task. By contrast, this paper aims to investigate the tas…
Improving Disfluency Detection by Self-Training a Self-Attentive Model
Paria Jamshid Lou, Mark Johnson
Self-attentive neural syntactic parsers using contextualized word embeddings (e.g. ELMo or BERT) currently produce state-of-the-art results in joint parsing and disfluency detectio…
How to best use Syntax in Semantic Role Labelling
Yufei Wang, Mark Johnson, Stephen Wan +2
There are many different ways in which external information might be used in an NLP task. This paper investigates how external syntactic information can be used most effectively in…
nocaps: novel object captioning at scale
Harsh Agrawal, Karan Desai, Yufei Wang +7
Image captioning models have achieved impressive results on datasets containing limited visual concepts and large amounts of paired image-caption training data. However, if these m…