Cross-Domain Label-Adaptive Stance Detection
- URL: http://arxiv.org/abs/2104.07467v1
- Date: Thu, 15 Apr 2021 14:04:29 GMT
- Title: Cross-Domain Label-Adaptive Stance Detection
- Authors: Momchil Hardalov, Arnav Arora, Preslav Nakov, Isabelle Augenstein
- Abstract summary: Stance detection concerns the classification of a writer's viewpoint towards a target.
In this paper, we perform an in-depth analysis of 16 stance detection datasets.
We propose an end-to-end unsupervised framework for out-of-domain prediction of unseen, user-defined labels.
- Score: 32.800766653254634
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Stance detection concerns the classification of a writer's viewpoint towards
a target. There are different task variants, e.g., stance of a tweet vs. a full
article, or stance with respect to a claim vs. an (implicit) topic. Moreover,
task definitions vary, which includes the label inventory, the data collection,
and the annotation protocol. All these aspects hinder cross-domain studies, as
they require changes to standard domain adaptation approaches. In this paper,
we perform an in-depth analysis of 16 stance detection datasets, and we explore
the possibility for cross-domain learning from them. Moreover, we propose an
end-to-end unsupervised framework for out-of-domain prediction of unseen,
user-defined labels. In particular, we combine domain adaptation techniques
such as mixture of experts and domain-adversarial training with label
embeddings, and we demonstrate sizable performance gains over strong baselines
-- both (i) in-domain, i.e., for seen targets, and (ii) out-of-domain, i.e.,
for unseen targets. Finally, we perform an exhaustive analysis of the
cross-domain results, and we highlight the important factors influencing the
model performance.
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