The ApposCorpus: a new multilingual, multi-domain dataset for factual appositive generation

Yova Kementchedjhieva, Di Lu, Joel Tetreault


Abstract
News articles, image captions, product reviews and many other texts mention people and organizations whose name recognition could vary for different audiences. In such cases, background information about the named entities could be provided in the form of an appositive noun phrase, either written by a human or generated automatically. We expand on the previous work in appositive generation with a new, more realistic, end-to-end definition of the task, instantiated by a dataset that spans four languages (English, Spanish, German and Polish), two entity types (person and organization) and two domains (Wikipedia and News). We carry out an extensive analysis of the data and the task, pointing to the various modeling challenges it poses. The results we obtain with standard language generation methods show that the task is indeed non-trivial, and leaves plenty of room for improvement.
Anthology ID:
2020.coling-main.180
Volume:
Proceedings of the 28th International Conference on Computational Linguistics
Month:
December
Year:
2020
Address:
Barcelona, Spain (Online)
Venue:
COLING
SIG:
Publisher:
International Committee on Computational Linguistics
Note:
Pages:
1989–2003
Language:
URL:
https://aclanthology.org/2020.coling-main.180
DOI:
10.18653/v1/2020.coling-main.180
Bibkey:
Cite (ACL):
Yova Kementchedjhieva, Di Lu, and Joel Tetreault. 2020. The ApposCorpus: a new multilingual, multi-domain dataset for factual appositive generation. In Proceedings of the 28th International Conference on Computational Linguistics, pages 1989–2003, Barcelona, Spain (Online). International Committee on Computational Linguistics.
Cite (Informal):
The ApposCorpus: a new multilingual, multi-domain dataset for factual appositive generation (Kementchedjhieva et al., COLING 2020)
Copy Citation:
PDF:
https://aclanthology.org/2020.coling-main.180.pdf
Data
PoMo