Abstract

Querying a database to retrieve an answer, telling a robot to perform an action, or teaching a computer to play a game are tasks requiring communication with machines in a language interpretable by them. Here we consider the task of converting human language to a knowledge-base (KB) language for question-answering. While human languages are sequential in nature with latent structures, machine interpretable languages have explicit formal structures. The computational linguistics community has created several treebanks to understand the formal structures of human languages, e.g., universal dependencies. But are these helpful in deriving target language structures?

In the first part of the talk, I will discuss how to convert universal dependencies in multiple languages to both general-purpose and kb-executable logical forms. In the second part, I will present a neural model on how to induce task-specific natural language structures. I will discuss the similarities and differences between linguists-defined and machine-induced structures.