2.7k citations · 6.8k across the 64 of their papers we have counts for
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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…
Better Document-Level Machine Translation with Bayes' Rule
Lei Yu, Laurent Sartran, Wojciech Stokowiec +4
We show that Bayes' rule provides an effective mechanism for creating document translation models that can be learned from only parallel sentences and monolingual documents---a com…
A Critical Analysis of Biased Parsers in Unsupervised Parsing
Chris Dyer, Gábor Melis, Phil Blunsom
A series of recent papers has used a parsing algorithm due to Shen et al. (2018) to recover phrase-structure trees based on proxies for "syntactic depth." These proxy depths are ob…
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…
Scalable Syntax-Aware Language Models Using Knowledge Distillation
Adhiguna Kuncoro, Chris Dyer, Laura Rimell +2
Prior work has shown that, on small amounts of training data, syntactic neural language models learn structurally sensitive generalisations more successfully than sequential langua…
Learning and Evaluating General Linguistic Intelligence
Dani Yogatama, Cyprien de Masson d'Autume, Jerome Connor +8
We define general linguistic intelligence as the ability to reuse previously acquired knowledge about a language's lexicon, syntax, semantics, and pragmatic conventions to adapt to…