Question Answering Fit for Purpose: A Perspective From Natural Language Processing and User Modeling
TL;DR
Past work in the fields of NLG and UM is looked at, outlining what was identified as important for graceful human machine interactions, and future research directions that I believe are important are discussed.
Abstract
Providing appropriate answers to questions is necessary in many situations, not just in the conversational AI systems we see and develop today. Research in Natural Language Processing (NLP) and User Modeling (UM) have investigated this topic for decades, starting in the era of ''symbolic AI''. While NLP in general was needed for the whole interaction (understanding the question and answering it), Natural Language Generation (NLG) was particularly concerned with providing good and coherent answers appropriate for the information need and the intended audience, which is where UM also played a role. At that time, information to include in the answers typically came from knowledge bases or data bases. Information Retrieval (IR) then was concerned with retrieving the documents (and later websites) most relevant to a query. As the amount of data and number of documents increased, information needs from users became increasingly complex. As a result, it seemed that combining advances in both NLP and IR was required. And of course, now, research often spans these two fields. In this talk, I will look at past work in the fields of NLG and UM, outlining what was identified as important for graceful human machine interactions. The game has changed now, of course, with LLMs and generative AI, which can do much that we could not do before. But some old questions remain unanswered, there are new questions (especially given the ''black box'' nature of LLMs), and we can probably learn from some earlier work. I will also discuss future research directions that I believe are important.