Decision Support Question Answering     

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