  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.
