Explanations

Coming Soon: Worldtree Corpus of Explanation Graphs for Elementary Science Questions (September 2017 snapshot)
University of Arizona & Allen Institute for Artificial Intelligence
This is the September 2017 snapshot of the Worldtree corpus of explanation graphs, explanatory role ratings, and associated tablestore, from the paper Worldtree: A Corpus of Explanation Graphs for Elementary Science Questions (submitted). Explanation graphs for 1,680 questions, and 4,950 tablestore rows across 62 semi-structured tables are provided. This data is intended to be paired with the AI2 Mercury and Licensed questions. Questions from the Mercury set are included, however the Licensed question set requires accepting a click-through license on the AI2 website, and the text of these licensed questions is removed from the question file here, but can be easily restored by matching the question IDs with the licensed question set after download. Explanations and all other annotation for the Licensed questions is still included in this dataset.
[ Coming Soon ]

Coming Soon: Ratings of Lexical Connection Quality for Information Aggregation
University of Arizona
This is the supplementary dataset for the paper Characterizing Lexical Overlap as a Method for Explanation Building: Lessons from Elementary Science (submitted). These ratings were used to generate average utility ratings in Tables 3 and 4.
[ Coming Soon ]

Common Explanatory Patterns (AKBC 2017)
University of Arizona
This is the supplementary dataset for the paper A Study of Automatically Acquiring Explanatory Inference Patterns from Corpora of Explanations: Lessons from Elementary Science (AKBC 2017). The dataset contains: (a) common explanatory patterns (i.e. patterns found more than once) in the first 800 questions of the September 2017 WorldTree corpus described in Section 3.1, and (b) A fine-grained characterization of reconstruction quality by number of edges in the gold graph, as an expansion to Figure 4.
[ AKBC2017_ExplanatoryPatterns_Nov2017.zip ]

Explanations for Science Questions (COLING 2016)
University of Arizona, Stony Brook University & Allen Institute for Artificial Intelligence
This is the dataset for the paper What’s in an Explanation? Characterizing Knowledge and Inference Requirements for Elementary Science Exams (COLING 2016). The data contains: (a) 1,363 gold explanation sentences supporting 363 science questions, (b) relation annotation for a subset of those explanations, and (c) a graphical annotation tool with annotation guidelines.
[ COLING2016_Explanations_Oct2016.zip ]