Decision Support Question Answering       

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Sugato Bagchi photo Kenneth J. (Ken) Barker photoBranimir K. Boguraev photo Mihaela A. Bornea photo Bhavani S Iyer 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