Abstract
We present a new annotation method for collecting data on relation inference in context. We convert the inference task to one of simple factoid question answering, allowing us to easily scale up to 16,000 high-quality examples. Our method corrects a major bias in previous evaluations, making our dataset much more realistic.
| Original language | English |
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| Title of host publication | 54th Annual Meeting of the Association for Computational Linguistics, ACL 2016 - Short Papers |
| Publisher | Association for Computational Linguistics (ACL) |
| Pages | 249-255 |
| Number of pages | 7 |
| ISBN (Electronic) | 9781510827592 |
| DOIs | |
| State | Published - 2016 |
| Event | 54th Annual Meeting of the Association for Computational Linguistics, ACL 2016 - Berlin, Germany Duration: 7 Aug 2016 → 12 Aug 2016 |
Publication series
| Name | 54th Annual Meeting of the Association for Computational Linguistics, ACL 2016 - Short Papers |
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Conference
| Conference | 54th Annual Meeting of the Association for Computational Linguistics, ACL 2016 |
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| Country/Territory | Germany |
| City | Berlin |
| Period | 7/08/16 → 12/08/16 |
Bibliographical note
Publisher Copyright:© 2016 Association for Computational Linguistics.