@inproceedings{ren-etal-2021-cross,
title = "Cross-Topic Rumor Detection using Topic-Mixtures",
author = "Ren, Xiaoying and
Jiang, Jing and
Serena Khoo, Ling Min and
Chieu, Hai Leong",
editor = "Merlo, Paola and
Tiedemann, Jorg and
Tsarfaty, Reut",
booktitle = "Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume",
month = apr,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.eacl-main.131",
doi = "10.18653/v1/2021.eacl-main.131",
pages = "1534--1538",
abstract = "There has been much interest in rumor detection using deep learning models in recent years. A well-known limitation of deep learning models is that they tend to learn superficial patterns, which restricts their generalization ability. We find that this is also true for cross-topic rumor detection. In this paper, we propose a method inspired by the {``}mixture of experts{''} paradigm. We assume that the prediction of the rumor class label given an instance is dependent on the topic distribution of the instance. After deriving a vector representation for each topic, given an instance, we derive a {``}topic mixture{''} vector for the instance based on its topic distribution. This topic mixture is combined with the vector representation of the instance itself to make rumor predictions. Our experiments show that our proposed method can outperform two baseline debiasing methods in a cross-topic setting. In a synthetic setting when we removed topic-specific words, our method also works better than the baselines, showing that our method does not rely on superficial features.",
}
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<abstract>There has been much interest in rumor detection using deep learning models in recent years. A well-known limitation of deep learning models is that they tend to learn superficial patterns, which restricts their generalization ability. We find that this is also true for cross-topic rumor detection. In this paper, we propose a method inspired by the “mixture of experts” paradigm. We assume that the prediction of the rumor class label given an instance is dependent on the topic distribution of the instance. After deriving a vector representation for each topic, given an instance, we derive a “topic mixture” vector for the instance based on its topic distribution. This topic mixture is combined with the vector representation of the instance itself to make rumor predictions. Our experiments show that our proposed method can outperform two baseline debiasing methods in a cross-topic setting. In a synthetic setting when we removed topic-specific words, our method also works better than the baselines, showing that our method does not rely on superficial features.</abstract>
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%0 Conference Proceedings
%T Cross-Topic Rumor Detection using Topic-Mixtures
%A Ren, Xiaoying
%A Jiang, Jing
%A Serena Khoo, Ling Min
%A Chieu, Hai Leong
%Y Merlo, Paola
%Y Tiedemann, Jorg
%Y Tsarfaty, Reut
%S Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume
%D 2021
%8 April
%I Association for Computational Linguistics
%C Online
%F ren-etal-2021-cross
%X There has been much interest in rumor detection using deep learning models in recent years. A well-known limitation of deep learning models is that they tend to learn superficial patterns, which restricts their generalization ability. We find that this is also true for cross-topic rumor detection. In this paper, we propose a method inspired by the “mixture of experts” paradigm. We assume that the prediction of the rumor class label given an instance is dependent on the topic distribution of the instance. After deriving a vector representation for each topic, given an instance, we derive a “topic mixture” vector for the instance based on its topic distribution. This topic mixture is combined with the vector representation of the instance itself to make rumor predictions. Our experiments show that our proposed method can outperform two baseline debiasing methods in a cross-topic setting. In a synthetic setting when we removed topic-specific words, our method also works better than the baselines, showing that our method does not rely on superficial features.
%R 10.18653/v1/2021.eacl-main.131
%U https://aclanthology.org/2021.eacl-main.131
%U https://doi.org/10.18653/v1/2021.eacl-main.131
%P 1534-1538
Markdown (Informal)
[Cross-Topic Rumor Detection using Topic-Mixtures](https://aclanthology.org/2021.eacl-main.131) (Ren et al., EACL 2021)
ACL
- Xiaoying Ren, Jing Jiang, Ling Min Serena Khoo, and Hai Leong Chieu. 2021. Cross-Topic Rumor Detection using Topic-Mixtures. In Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pages 1534–1538, Online. Association for Computational Linguistics.