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.
BibTeX
@inproceedings{kementchedjhieva-etal-2020-apposcorpus,
title = "The {A}ppos{C}orpus: a new multilingual, multi-domain dataset for factual appositive generation",
author = "Kementchedjhieva, Yova and
Lu, Di and
Tetreault, Joel",
editor = "Scott, Donia and
Bel, Nuria and
Zong, Chengqing",
booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
month = dec,
year = "2020",
address = "Barcelona, Spain (Online)",
publisher = "International Committee on Computational Linguistics",
url = "https://aclanthology.org/2020.coling-main.180/",
doi = "10.18653/v1/2020.coling-main.180",
pages = "1989--2003"
}