TL;DR: We propose controllable counterfactuals (CoCo) to evaluate dialogue state tracking (DST) models on novel scenarios, which results in significant performance drop of up to 30.8% for state-of-the-art DST models. Using CoCo for…
TL; DR: We find that current self-supervised learning approaches suffer from poor visual grounding and receive improper supervisory signal when trained on complex scene images. We introduce CAST to improve visual grounding during…
TL; DR: We propose a new semi-supervised learning method which achieves state-of-the-art performance by learning jointly-evolved class probabilities and image representations. What are the existing semi-supervised learning methods? Semi-supervised learning aims to leverage…
In recent years, the natural language processing (NLP) community has seen the development of increasingly powerful language models [1, 2], capable of generating textual output that is indistinguishable from human-written text. This includes…
