COLING 2020main11 citations
Leveraging WordNet Paths for Neural Hypernym Prediction
Yejin Cho, Juan Diego Rodriguez, Yifan Gao, Katrin Erk
Abstract
We formulate the problem of hypernym prediction as a sequence generation task, where the sequences are taxonomy paths in WordNet. Our experiments with encoder-decoder models show that training to generate taxonomy paths can improve the performance of direct hypernym prediction. As a simple but powerful model, the hypo2path model achieves state-of-the-art performance, outperforming the best benchmark by 4.11 points in hit-at-one (H@1).
BibTeX
@inproceedings{cho-etal-2020-leveraging,
title = "Leveraging {W}ord{N}et Paths for Neural Hypernym Prediction",
author = "Cho, Yejin and
Rodriguez, Juan Diego and
Gao, Yifan and
Erk, Katrin",
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.268/",
doi = "10.18653/v1/2020.coling-main.268",
pages = "3007--3018"
}