WikiPossessions: Possession Timeline Generation as an Evaluation Benchmark for Machine Reading Comprehension of Long Texts

Dhivya Chinnappa, Alexis Palmer, Eduardo Blanco


Abstract
This paper presents WikiPossessions, a new benchmark corpus for the task of temporally-oriented possession (TOP), or tracking objects as they change hands over time. We annotate Wikipedia articles for 90 different well-known artifacts paintings, diamonds, and archaeological artifacts), producing 799 artifact-possessor relations with associated attributes. For each article, we also produce a full possession timeline. The full version of the task combines straightforward entity-relation extraction with complex temporal reasoning, as well as verification of textual support for the relevant types of knowledge. Specifically, to complete the full TOP task for a given article, a system must do the following: a) identify possessors; b) anchor possessors to times/events; c) identify temporal relations between each temporal anchor and the possession relation it corresponds to; d) assign certainty scores to each possessor and each temporal relation; and e) assemble individual possession events into a global possession timeline. In addition to the corpus, we release evaluation scripts and a baseline model for the task.
Anthology ID:
2020.lrec-1.140
Volume:
Proceedings of the 12th Language Resources and Evaluation Conference
Month:
May
Year:
2020
Address:
Marseille, France
Venue:
LREC
SIG:
Publisher:
European Language Resources Association
Note:
Pages:
1110–1117
Language:
English
URL:
https://aclanthology.org/2020.lrec-1.140
DOI:
Bibkey:
Cite (ACL):
Dhivya Chinnappa, Alexis Palmer, and Eduardo Blanco. 2020. WikiPossessions: Possession Timeline Generation as an Evaluation Benchmark for Machine Reading Comprehension of Long Texts. In Proceedings of the 12th Language Resources and Evaluation Conference, pages 1110–1117, Marseille, France. European Language Resources Association.
Cite (Informal):
WikiPossessions: Possession Timeline Generation as an Evaluation Benchmark for Machine Reading Comprehension of Long Texts (Chinnappa et al., LREC 2020)
Copy Citation:
PDF:
https://aclanthology.org/2020.lrec-1.140.pdf