Name
Abstract | ||
|---|---|---|
Textual knowledge bases such as Wikipedia require considerable effort to keep up to date and consistent. While automated writing assistants could potentially ease this burden, the problem of suggesting edits grounded in external knowledge has been under-explored. In this paper, we introduce the novel generation task of *faithfully reflecting updated information in text* (FRUIT) where the goal is to update an existing article given new evidence. We release the FRUIT-WIKI dataset, a collection of over 170K distantly supervised data produced from pairs of Wikipedia snapshots, along with our data generation pipeline and a gold evaluation set of 914 instances whose edits are guaranteed to be supported by the evidence. We provide benchmark results for popular generation systems as well as EDIT5 -- a T5-based approach tailored to editing we introduce that establishes the state of the art. Our analysis shows that developing models that can update articles faithfully requires new capabilities for neural generation models, and opens doors to many new applications. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.18653/V1/2022.NAACL-MAIN.269 | North American Chapter of the Association for Computational Linguistics (NAACL) |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Robert L. Logan IV | 1 | 0 | 0.34 |
| Alexandre Passos | 2 | 0 | 0.68 |
| Sameer Singh | 3 | 1060 | 71.63 |
| Ming-Wei Chang | 4 | 0 | 0.34 |