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The Greedy and Recursive Search for Morphological Productivity
This work proposes a greedy search model that automatically hypothesizes rules and evaluates their productivity over a vocabulary and produces responses to nonce words that are more similar to those of human subjects than current neural network models’ responses are.
Learning Morphological Productivity as Meaning-Form Mappings
Child language acquisition is famously accurate despite the sparsity of linguistic input. In this paper, we introduce a cognitively motivated method for morphological acquisition with a special focus
ADAM: A Sandbox for Implementing Language Learning
The architecture of the ADAM system is described in detail, and its components are illustrated with examples to help design and test different language learning curricula as well as learning algorithms.
A Grounded Approach to Modeling Generic Knowledge Acquisition
A computational framework designed to model grounded language acquisition is extended by introducing the concept network, which enables the system to encode knowledge learned from generic statements and represent the associations between concepts learned by the system.