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Modeling And Learning Agents That Understand Language In Context

Abstract: The meaning of a natural language utterance is influenced by the context in which it occurs, including interaction history and situated context. I will discuss two recent projects in context-dependent natural language understanding for building natural language interfaces to databases and following sequences of instructions. In the first part, I will introduce a model for mapping from natural language to executable SQL queries in an interaction. To resolve the meaning of later utterances, the system must consider the interaction history, including previous user utterances and previously-generated queries. We show how using both implicit and explicit mechanisms for making use of interaction history allows the system to effectively generate context-dependent representations. In the second part, I will describe an approach to map sequences of natural language instructions to system actions that modify an environment, focusing on learning without direct supervision on action sequences. We introduce an exploration-based learning approach that effectively learns to compose system actions to carry out user instructions in context of the environment and interaction.

Bio: Alane Suhr is a PhD student in the Computer Science department at Cornell University, focusing on building agents that understand natural language grounded in complex interactions. She is the recipient of an AI2 Key Scientific Challenges Award and a Microsoft Research Women's Fellowship, and is a National Science Foundation Graduate Research Fellow. She has received paper awards at ACL 2017 and NAACL 2018. Alane received a Bachelor's degree in Computer Science and Engineering from Ohio State University in 2016.