The overarching goal of ThReDS was to build a computer system that can 'refer', i.e. generate descriptions of concepts or entities in a way that uniquely identifies them for a human (e.g. Harry Potter = "the wizard with the round glasses", Hermione = "the best student at Hogwarts", etc). Having such a system would let us understand better how humans communicate with each other, and help us build artificial agents that can converse with us.
Before an artificial agent can talk, it needs to learn about the world, just as a child would do. In linguistic and computational terms, this means acquiring *representations* of the things the agent is exposed to. In the field of Distributional Semantics, such computational representations have traditionally been built from raw text data (sometimes enriched with visual information) and take the form of a 'vector', that is, a mathematical model of the way a particular word is used by human beings, as experienced by the agent. Such vectors can be found in many everyday applications like search engines, recommendation systems and conversational agents. So far, however, they have only been constructed for *concepts* (e.g. 'student', 'owl', 'broom') rather than individual entities ('Harry Potter', 'Hedwig', 'Harry's Nimbus 2000'). This is because current algorithms need considerable amounts of data to learn properly, and references to individual entities are much less frequent in raw text than generic occurrences of words. Further, those raw vector representations are not suitable to refer from, because they do not explicitly encapsulate the properties of the concept or individual that a human would use to identify them (e.g. 'wearing glasses' for Harry Potter). In order to make vectors compatible with so-called 'Referring Expression Generation' systems, that is, algorithms that can produce successful references to things in the world, a translation must be found to a more formal and structured representation of meaning, which in theoretical linguistics has its incarnation in 'Model-theoretic Semantics'.
ThReDS tackled two challenges: a) the computational extraction of representations of entities from raw text, concentrating on the small data issue; b) the theoretical account of how raw exposure to linguistic data (distributional semantics information) can shape the agent's representation of the world (their model-theoretic semantics).
