Open-text semantic parsers are designed to interpret any statement in natural language by inferring a corresponding meaning repre- sentation (MR – a formal representation of its sense). Unfortunately, large scale systems cannot be easily machine-learned due to a 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 frame- work. Experiments on these various tasks in- dicate the promise of the approach.

Does not target a specific nonlinguistic domain.