Search Results for author: Marc van Zee

Found 5 papers, 3 papers with code

Compositional Generalization in Semantic Parsing: Pre-training vs. Specialized Architectures

1 code implementation17 Jul 2020 Daniel Furrer, Marc van Zee, Nathan Scales, Nathanael Schärli

While mainstream machine learning methods are known to have limited ability to compositionally generalize, new architectures and techniques continue to be proposed to address this limitation.

Language Modelling Semantic Parsing

Intention as Commitment toward Time

no code implementations17 Apr 2020 Marc van Zee, Dragan Doder, Leendert van der Torre, Mehdi Dastani, Thomas Icard, Eric Pacuit

The first contribution is a logic for reasoning about intention, time and belief, in which assumptions of intentions are represented by preconditions of intended actions.

Measuring Compositional Generalization: A Comprehensive Method on Realistic Data

2 code implementations ICLR 2020 Daniel Keysers, Nathanael Schärli, Nathan Scales, Hylke Buisman, Daniel Furrer, Sergii Kashubin, Nikola Momchev, Danila Sinopalnikov, Lukasz Stafiniak, Tibor Tihon, Dmitry Tsarkov, Xiao Wang, Marc van Zee, Olivier Bousquet

We present a large and realistic natural language question answering dataset that is constructed according to this method, and we use it to analyze the compositional generalization ability of three machine learning architectures.

BIG-bench Machine Learning Question Answering

AGM-Style Revision of Beliefs and Intentions from a Database Perspective (Preliminary Version)

no code implementations25 Apr 2016 Marc van Zee, Dragan Doder

We show in a representation theorem that a revision operator satisfying our postulates can be represented by a pre-order on interpretations of the beliefs, together with a selection function for the intentions.

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