36 citations · 44 across the 8 of their papers we have counts for
4 papers · 1 filter
Make Up Your Mind! Adversarial Generation of Inconsistent Natural Language Explanations
Oana-Maria Camburu, Brendan Shillingford, Pasquale Minervini +2
To increase trust in artificial intelligence systems, a promising research direction consists of designing neural models capable of generating natural language explanations for the…
Can I Trust the Explainer? Verifying Post-hoc Explanatory Methods
Oana-Maria Camburu, Eleonora Giunchiglia, Jakob Foerster +2
For AI systems to garner widespread public acceptance, we must develop methods capable of explaining the decisions of black-box models such as neural networks. In this work, we ide…
WikiCREM: A Large Unsupervised Corpus for Coreference Resolution
Vid Kocijan, Oana-Maria Camburu, Ana-Maria Cretu +3
Pronoun resolution is a major area of natural language understanding. However, large-scale training sets are still scarce, since manually labelling data is costly. In this work, we…
A Surprisingly Robust Trick for Winograd Schema Challenge
Vid Kocijan, Ana-Maria Cretu, Oana-Maria Camburu +2
The Winograd Schema Challenge (WSC) dataset WSC273 and its inference counterpart WNLI are popular benchmarks for natural language understanding and commonsense reasoning. In this p…