Towards Open-Text Semantic Parsing via Multi-Task Learning of Structured Embeddings

Antoine Bordes; Xavier Glorot; Jason Weston; Yoshua Bengio

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arXiv · 1107.3663

Towards Open-Text Semantic Parsing via Multi-Task Learning of Structured Embeddings

Antoine Bordes·Xavier Glorot·Jason Weston·Yoshua Bengio

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Abstract

Open-text (or open-domain) semantic parsers are designed to interpret any statement in natural language by inferring a corresponding meaning representation (MR). Unfortunately, large scale systems cannot be easily machine-learned due to lack of directly supervised data. We propose here a method that learns to assign MRs to a wide range of text (using a dictionary of more than 70,000 words, which are mapped to more than 40,000 entities) thanks to a training scheme that combines learning from WordNet and ConceptNet with learning from raw text. The model learns structured embeddings of words, entities and MRs via a multi-task training process operating on these diverse sources of data that integrates all the learnt knowledge into a single system. This work ends up combining methods for knowledge acquisition, semantic parsing, and word-sense disambiguation. Experiments on various tasks indicate that our approach is indeed successful and can form a basis for future more sophisticated systems.

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Antoine Bordes, Xavier Glorot, Jason Weston, Yoshua Bengio. 2011-07-19. Towards Open-Text Semantic Parsing via Multi-Task Learning of Structured Embeddings. https://arxiv.org/abs/1107.3663

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