Name
Title | ||
|---|---|---|
Generating Factoid Questions With Recurrent Neural Networks: The 30m Factoid Question-Answer Corpus |
Abstract | ||
|---|---|---|
Over the past decade, large-scale supervised learning corpora have enabled machine learning researchers to make substantial advances. However, to this date, there are no large-scale question-answer corpora available. In this paper we present the 30M Factoid Question-Answer Corpus, an enormous question-answer pair corpus produced by applying a novel neural network architecture on the knowledge base Freebase to transduce facts into natural language questions. The produced question-answer pairs are evaluated both by human evaluators and using automatic evaluation metrics, including well-established machine translation and sentence similarity metrics. Across all evaluation criteria the question-generation model outperforms the competing template-based baseline. Furthermore, when presented to human evaluators, the generated questions appear to be comparable in quality to real human-generated questions. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.18653/v1/P16-1056 | PROCEEDINGS OF THE 54TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, VOL 1 |
DocType | Volume | Citations |
Conference | abs/1603.06807 | 34 |
PageRank | References | Authors |
1.06 | 21 | 7 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Iulian Vlad Serban | 1 | 854 | 30.73 |
| Alberto García-Durán | 2 | 53 | 1.70 |
| Çaglar Gülçehre | 3 | 3010 | 133.22 |
| Sungjin Ahn | 4 | 140 | 6.73 |
| Sarath Chandar A. P. | 5 | 127 | 13.79 |
| Aaron C. Courville | 6 | 6671 | 348.46 |
| Yoshua Bengio | 7 | 42677 | 3039.83 |