Joint Learning of Words and Meaning Representations for Open-Text Semantic Parsing
; Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics, PMLR 22:127-135, 2012.
Open-text semantic parsers are designed to interpret any statement in natural language by inferring a corresponding meaning representation (MR - a formal representation of its sense). Unfortunately, large scale systems cannot be easily machine-learned due to lack of directly supervised data. We propose a method that learns to assign MRs to a wide range of text (using a dictionary of more than 70,000 words mapped to more than 40,000 entities) thanks to a training scheme that combines learning from knowledge bases (e.g. WordNet) with learning from raw text. The model jointly learns representations of words, entities and MRs via a multi-task training process operating on these diverse sources of data. Hence, the system ends up providing methods for knowledge acquisition and word-sense disambiguation within the context of semantic parsing in a single elegant framework. Experiments on these various tasks indicate the promise of the approach.