Most digital assistants that follow NL instructions focus on uncovering concrete pred-arg structures, such as "book a flight" or "send a mail". However, when humans convey complex instructions they often resort to abstractions, such as emerging shapes, iterations, conditions 1/n Can #nlproc models learn to interpret such abstract structures, and ground them in a concrete world?

We set out to elicit non-trivial natural lang instructions that contain multiple levels of abstraction, and evaluate models ability to execute them.
arxiv.org/abs/2106.14321

2/n

#abstraction in #NLProc: a thread >>

https://twitter.com/rtsarfaty/status/1409762792576536586

According to Wing 2011, #abstraction in natural language is the act of referring to a set of elements via their shared properties, discarding irrelevant distinctions.

It can be a shape they make ("object"), order of traversal ("control structure") or a condition they all meet>>