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Found 5 papers, 3 papers with code
Scaling Up Models and Data with $\texttt{t5x}$ and $\texttt{seqio}$
Recent neural network-based language models have benefited greatly from scaling up the size of training datasets and the number of parameters in the models themselves.
Compositional Generalization in Semantic Parsing: Pre-training vs. Specialized Architectures
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.
Intention as Commitment toward Time
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
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.
Ranked #5 on
Semantic Parsing
on CFQ
AGM-Style Revision of Beliefs and Intentions from a Database Perspective (Preliminary Version)
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.
