Abstract
People turn to the Web to seek answers to a wide variety of questions, a significant portion of which being“how to” questions. For a given “how to” question, rather than one single canonical set of instructions that satisfies everyone, there can be variations catering to the different needs of different people. Indeed, there are a growing number of popular websites where users submit and review instructions as varied as building a table and baking a pie. In addition to providing their subjective evaluation, reviewers often provide actionable refinements. These refinements clarify, correct, improve, or provide alternatives to the original instructions. However, identifying and reading all relevant reviews is a daunting task for a user. In this paper, we propose a generative model that jointly identifies user-proposed refinements in instruction reviews at multiple granularities, and aligns them to the appropriate steps in the original instructions. We view this as the first step towards addressing the more general task of identifying actionable information from unrestricted sources, and help users consume such information in a way that best suits their personal needs.

Biography
Bo Pang is a research scientist at Google. She obtained her PhD in Computer Science from Cornell University in 2006. Her primary research interests are in natural language processing. Her past work include sentiment analysis and opinion mining, paraphrasing, querylog analysis, bridging structured and unstructured data, personalized text consumption, and computational advertising