TabMCQ: A Dataset of General Knowledge Tables and Multiple-choice Questions
This resource supports researchers in question answering and related applications like information extraction, but it is incremental as it focuses on a specific domain without new methods.
The authors introduced TabMCQ, a dataset of curated tables and crowd-sourced multiple-choice questions for general knowledge reasoning in 4th grade science exams, including implicit alignment information between questions and tables.
We describe two new related resources that facilitate modelling of general knowledge reasoning in 4th grade science exams. The first is a collection of curated facts in the form of tables, and the second is a large set of crowd-sourced multiple-choice questions covering the facts in the tables. Through the setup of the crowd-sourced annotation task we obtain implicit alignment information between questions and tables. We envisage that the resources will be useful not only to researchers working on question answering, but also to people investigating a diverse range of other applications such as information extraction, question parsing, answer type identification, and lexical semantic modelling.