Decision Support Question Answering       


Sugato Bagchi photo Kenneth J. (Ken) Barker photoBranimir K. Boguraev photo Mihaela A. Bornea photoAdam Faulkner photo Oren Melamud photoNathaniel  (Nat) Mills photo Sara Rosenthal photo

Decision Support Question Answering - overview

The Natural Language Analytics team pursues basic research in Natural Language Processing. The goal of the research is to create a new paradigm for Question Answering systems as active collaborators in information gathering for complex decision making.

Existing question answering (QA) systems:

  • assume that the decision problem can be directly formulated into a question and that the answer can directly inform the decision
  • are one-shot systems that can’t build on prior interactions
  • are black boxes that do not expose their interpretation and process
  • don’t learn/adapt from interactions with an end user

To address the limitations of existing QA systems, the team is conducting research into a new kind of exploratory question answering system with the following properties:

  • Contextual: takes into consideration arbitrary context representing background information that will inform question answering behavior
  • Transparent: exposes elements of interpretation so it is clear why the system behaves a certain way
  • Guidable: allows intervention and guidance to improve question answering relevance
  • Iterative: builds on a persistent context, taking into consideration previous statements, questions and their results
  • Adaptable: learns from user behavior, allowing improvement on interpretations of user input and the question answering results produced

Research Areas

Artificial Intelligence