On our best behaviour

By Hector J. Levesque. 2014

113 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Cognitive Science & Psychology

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Citations in the corpus

A large annotated corpus for learning natural language inference (2015, background): “Some of these examples have qualities reminiscent of Winograd schemas (Winograd, 1972; Levesque, 2013).”
Adversarial Examples for Evaluating Reading Comprehension Systems (2017, background): “Quantifying the extent to which a computer sys-tem exhibits intelligent behavior is a longstanding problem in AI (Levesque, 2013).”
Stress Test Evaluation for Natural Language Inference (2018, background): “Unlike work on manual construction of small adversarial evaluation sets for various NLP tasks (Levesque, 2014; Mahler et al., 2017; Staliūnaite and Bonfil, 2017; Isabelle et al., 2017; Belinkov and Bisk, 2017; Bawden et al., 2017), our work focuses on a”
What Makes Reading Comprehension Questions Easier? (2018, methodology): “Our heuristics and annotation are motivated by un-intended biases (Levesque, 2014) and evaluation overﬁtting (Whiteson et al., 2011), respectively.”
DynaSent: A Dynamic Benchmark for Sentiment Analysis (2020, background): “Challenge and adversarial datasets (Winograd, 1972; Levesque, 2013) have risen to prominence in response to the sense that benchmark results are over-stating the quality of the models we are developing (Linzen, 2020).”
Evaluating adversarial attacks against multiple fact verification systems (2019, background): “Manual construction informed by expert knowledge: Instances that exploit world knowledge, semantics, pragmatics, morphology and syntactic variation have been written and compiled into challenge datasets for Machine Translation (Isabelle et al., 2017), Sentiment Analysis (Mahler et al., 2017; Stali¯u”
Adversarial attacks against Fact Extraction and VERification (2019, methodology): “Manual Construction Small adversarial datasets have been manually constructed and successfully used to identify limitations in Machine Translation [3, 11], Sentiment Analysis [15, 25] and Natural Language Understanding [14] systems.”
Gender Bias in Machine Translation (2021, background): “Neural networks rely on easy-to-learn shortcuts or ‘‘cheap tricks’’ (Levesque, 2014), as picking up on spurious correlations offered by training data can be easier for machines than learning to actually solve a specific task.”
On Making Reading Comprehension More Comprehensive (2019, unknown): “Instead we turn to ideas that go back at least to Alan Turing’s test for machine intelligence (Turing, 1950; Levesque, 2013)—it is through interacting in natural language that an entity can demon-”
Differentiable Reasoning on Large Knowledge Bases and Natural Language (2019, background): “The main focus of Artiﬁcial Intelligence is building systems that exhibit intelligent behaviour (Levesque 2014).”
The Winograd Schema Challenge (2012, methodology): “In that paper, as well as in (Levesque, Davis, and Morgenstern 2012) and in his Research Excellence lecture at IJCAI 2013 (Levesque 2014), Levesque proposed the WSC both as an alternative to the Turing Test and as a measure for progress in commonsense reasoning.”
Provable Limitations of Acquiring Meaning from Ungrounded Form: What Will Future Language Models Understand? (2021, background): “Turing (1950) constitutes an early behaviorist perspective; more recent approaches tend to emphasize not just an ex-ternal view of a system’s behavior, but also “how it is achieved” (Levesque, 2014).”
Adversarially Regularising Neural NLI Models to Integrate Logical Background Knowledge (2018, background): “An open problem in Artiﬁcial Intelligence is quantifying the extent to which algorithms exhibit intelligent behaviour (Levesque, 2014).”
Evaluation in artificial intelligence: from task-oriented to ability-oriented measurement (2017, unknown): “For instance, the Winograd Schema Challenge is a commonsense reasoning task70 (Levesque et al, 2012; Levesque, 2014; Morgenstern et al, 2016) introduced by Hector Levesque in his 2013 IJCAI Research Excellence Address.”
Addressing the Winograd Schema Challenge as a Sequence Ranking Task (2018, background): “…Schema Challenge (WSC) targets difﬁcult pronoun resolution problems which are easy to resolve for humans, but represent a great challenge for AI systems because they require the application of cognitive inferencing in combination with world knowledge (Levesque et al., 2012; Levesque, 2014).”