A First-Order-Logic Based Model for Grounded Language Learning

• Published in: Advances in Intelligent Data Analysis XIV Vol. 9385; pp. 49 - 60
• Main Authors: Becerra-Bonache, Leonor, Blockeel, Hendrik, Galván, María, Jacquenet, François
• Format: Book Chapter
• Language: English
• Published: Switzerland Springer International Publishing AG 2015; Springer International Publishing
• Series: Lecture Notes in Computer Science
• Subjects: Artificial intelligence; Color Shape; Context Description; Image processing; Inductive Logic Programming; Machine Translation; Parallel Corpus; Pattern recognition
• ISBN: 3319244647; 9783319244648
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/978-3-319-24465-5_5

Much is still unknown about how children learn language, but it is clear that they perform “grounded” language learning: they learn the grammar and vocabulary not just from examples of sentences, but from examples of sentences in a particular context. Grounded language learning has been the subject of much research. Most of this work focuses on particular aspects, such as constructing semantic parsers, or on particular types of applications. In this paper, we take a broader view that includes an aspect that has received little attention until now: learning the meaning of phrases from phrase/context pairs in which the phrase’s meaning is not explicitly represented. We propose a simple model for this task that uses first-order logic representations for contexts and meanings, including a simple incremental learning algorithm. We experimentally demonstrate that the proposed model can explain the gradual learning of simple concepts and language structure, and that it can easily be used for interpretation, generation, and translation of phrases.