Name
Abstract | ||
|---|---|---|
There are a growing number of popular web sites where users submit and review instructions for completing tasks 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. Labeled data is not readily available for these tasks, so we focus on the unsupervised setting. In experiments in the recipe domain, our model provides 90.1% F1 for predicting refinements at the review level, and 77.0% F1 for predicting refinement segments within reviews. |
Year | Venue | Keywords |
|---|---|---|
2012 | ACL | actionable refinement,daunting task,mining refinement,labeled data,review instruction,review level,original instruction,generative model,relevant review,appropriate step,instruction review |
Field | DocType | Volume |
User-generated content,Spice,Computer science,Artificial intelligence,Recipe,Natural language processing,Labeled data,Machine learning,Generative model | Conference | P12-1 |
Citations | PageRank | References |
7 | 0.49 | 17 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Gregory Druck | 1 | 341 | 17.20 |
| Bo Pang | 2 | 5795 | 451.00 |