Archive for September 28th, 2017

Please join us for the next NLP Seminar on Monday, October 9, at 4:00pm in 202 South Hall.

Speaker: Siva Reddy (Stanford)

Title:  Linguists-defined vs. Machine-induced Natural Language Structures for Executable Semantic Parsing

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 languages to a knowledge-base (KB) language for question-answering. While human languages have 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 useful for deriving machine interpretable formal 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, and pros and cons of each.

Bio:

Siva Reddy is a postdoc at the Stanford NLP group working with Chris Manning. His research focuses on finding fundamental representations of language, mostly interpretable, which are useful for NLP applications, especially machine understanding. In this direction, he is currently exploring whether linguistic representations are necessary or all we need is end-to-end learning. His postdoc is partly funded by a Facebook AI Research grant. Prior to the postdoc, he was a Google PhD Fellow at the University of Edinburgh under the supervision of Mirella Lapata and Mark Steedman. He worked with Google Parsing team as an intern during his PhD, and as a full-time employee for Adam Kilgarriff’s Sketch Engine before his PhD. His team won the first place in SemEval 2011 Compositionality Detection task and a best paper at IJCNLP 2011. Apart from language, he loves nature and badminton.