Papers with Wikipedia abstracts
Bootstrapping Generators from Noisy Data (N18-1)
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| Challenge: | Existing methods for data-to-text generation focus on learning correspondences between structured data and associated texts. |
| Approach: | They aim to bootstrap generators from large scale datasets where data and related texts are loosely aligned. |
| Outcome: | The proposed model improves on a vanilla encoder-decoder which relies on soft attention. |
T-REx: A Large Scale Alignment of Natural Language with Knowledge Base Triples (L18-1)
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Hady Elsahar, Pavlos Vougiouklis, Arslen Remaci, Christophe Gravier, Jonathon Hare, Frederique Laforest, Elena Simperl
| Challenge: | Existing datasets that provide alignments between natural language and knowledge bases (KB) triples are limited in size, lack coverage and are of unreported quality. |
| Approach: | They propose to build a large scale dataset of alignments between Wikipedia abstracts and Wikidata triples that is two orders of magnitude larger than the largest available alignments dataset. |
| Outcome: | The proposed dataset is two orders of magnitude larger than the largest available dataset and covers 2.5 times more predicates. |