QuAX

Abstract

Frequently Asked Questions (FAQ) are a form of semi-structured data that provides users with commonly requested information and enables several natural language processing tasks. Given the plethora of such question-answer pairs on the Web, there is an opportunity to automatically build large FAQ collections for any domain, such as COVID-19 or Plastic Surgery. These collections can be used by several information-seeking portals and applications, such as AI chatbots. Automatically identifying and extracting such high-utility question-answer pairs is a challenging endeavor, which has been tackled by little research work. For a question-answer pair to be useful to a broad audience, it must (i) provide general information -- not be specific to the Web site or Web page where it is hosted -- and (ii) must be self-contained -- not have references to other entities in the page or missing terms (ellipses) that render the question-answer pair ambiguous. Although identifying general, self-contained questions may seem like a straightforward binary classification problem, the limited availability of training data for this task and the countless domains make building machine learning models challenging. Existing efforts in extracting FAQs from the Web typically focus on FAQ retrieval without much regard to the utility of the extracted FAQ. We propose QuAX: a framework for extracting high-utility (i.e., general and self-contained) domain-specific FAQ lists from the Web. QuAX receives a set of keywords from a user, and works in a pipelined fashion to find relevant web pages and extract general and self-contained questions-answer pairs. We experimentally show how QuAX generates high-utility FAQ collections with little and domain-agnostic training data, and how the individual stages of the pipeline improve on the corresponding state-of-the-art.