@inproceedings{allaway-mckeown-2020-zero,
title = "{Z}ero-{S}hot {S}tance {D}etection: {A} {D}ataset and {M}odel using {G}eneralized {T}opic {R}epresentations",
author = "Allaway, Emily and
McKeown, Kathleen",
editor = "Webber, Bonnie and
Cohn, Trevor and
He, Yulan and
Liu, Yang",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.emnlp-main.717",
doi = "10.18653/v1/2020.emnlp-main.717",
pages = "8913--8931",
abstract = "Stance detection is an important component of understanding hidden influences in everyday life. Since there are thousands of potential topics to take a stance on, most with little to no training data, we focus on zero-shot stance detection: classifying stance from no training examples. In this paper, we present a new dataset for zero-shot stance detection that captures a wider range of topics and lexical variation than in previous datasets. Additionally, we propose a new model for stance detection that implicitly captures relationships between topics using generalized topic representations and show that this model improves performance on a number of challenging linguistic phenomena.",
}
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%0 Conference Proceedings
%T Zero-Shot Stance Detection: A Dataset and Model using Generalized Topic Representations
%A Allaway, Emily
%A McKeown, Kathleen
%Y Webber, Bonnie
%Y Cohn, Trevor
%Y He, Yulan
%Y Liu, Yang
%S Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F allaway-mckeown-2020-zero
%X Stance detection is an important component of understanding hidden influences in everyday life. Since there are thousands of potential topics to take a stance on, most with little to no training data, we focus on zero-shot stance detection: classifying stance from no training examples. In this paper, we present a new dataset for zero-shot stance detection that captures a wider range of topics and lexical variation than in previous datasets. Additionally, we propose a new model for stance detection that implicitly captures relationships between topics using generalized topic representations and show that this model improves performance on a number of challenging linguistic phenomena.
%R 10.18653/v1/2020.emnlp-main.717
%U https://aclanthology.org/2020.emnlp-main.717
%U https://doi.org/10.18653/v1/2020.emnlp-main.717
%P 8913-8931
Markdown (Informal)
[Zero-Shot Stance Detection: A Dataset and Model using Generalized Topic Representations](https://aclanthology.org/2020.emnlp-main.717) (Allaway & McKeown, EMNLP 2020)
ACL