Learning language through pictures

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Authors Ákos Kádár, Grzegorz Chrupała, Afra Alishahi
Journal/Conference Name IJCNLP 2015 7
Paper Category
Paper Abstract We propose Imaginet, a model of learning visually grounded representations of language from coupled textual and visual input. The model consists of two Gated Recurrent Unit networks with shared word embeddings, and uses a multi-task objective by receiving a textual description of a scene and trying to concurrently predict its visual representation and the next word in the sentence. Mimicking an important aspect of human language learning, it acquires meaning representations for individual words from descriptions of visual scenes. Moreover, it learns to effectively use sequential structure in semantic interpretation of multi-word phrases.
Date of publication 2015
Code Programming Language Python
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