Domain Adaptation for Stance Detection towards Unseen Target on Social Media

Deng, Ruofan; Panl, Li; Clavel, Chloé

doi:10.1109/acii55700.2022.9953818

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Outlook: Challenges and Opportunities1

Introduction1

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2023

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Cited by 2 publications

(2 citation statements)

References 18 publications

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“…Effectively incorporating transfer learning by pre-training on similar water quality station could be one of solutions to the resolve the issue. Leveraging domain adaptation techniques can help the LSTM generalize successfully ( Deng et al., 2022 )…”

Section: Outlook: Challenges and Opportunitiesmentioning

confidence: 99%

Long short-term memory models of water quality in inland water environments

Pyo,

Pachepsky,

Kim

et al. 2023

Water Research X

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Section: Outlook: Challenges and Opportunitiesmentioning

confidence: 99%

Long short-term memory models of water quality in inland water environments

Pyo,

Pachepsky,

Kim

et al. 2023

Water Research X

View full text Add to dashboard Cite

“…It has been extended when the targets can be a written concept or proposal [12,33]. Finally, Deng et al [34] proposed complex models for cross-target stance recognition, applied to a small set of specific targets.…”

Section: Introductionmentioning

confidence: 99%

Multilingual Multi-Target Stance Recognition in Online Public Consultations

Barrière¹,

Balahur

2023

Mathematics

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Machine Learning is an interesting tool for stance recognition in a large-scale context, in terms of data size, but also regarding the topics and themes addressed or the languages employed by the participants. Public consultations of citizens using online participatory democracy platforms offer this kind of setting and are good use cases for automatic stance recognition systems. In this paper, we propose to use three datasets of public consultations, in order to train a model able to classify the stance of a citizen within a text, towards a proposal or a debate question. We studied stance detection in several contexts: using data from an online platform without interactions between users, using multilingual data from online debates that are in one language, and using data from online intra-multilingual debates, which can contain several languages inside the same unique debate discussion. We propose several baselines and methods in order to take advantage of the different available data, by comparing the results of models using out-of-dataset annotations, and binary or ternary annotations from the target dataset. We finally proposed a self-supervised learning method to take advantage of unlabelled data. We annotated both the datasets with ternary stance labels and made them available.

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Domain Adaptation for Stance Detection towards Unseen Target on Social Media

Cited by 2 publications

References 18 publications

Long short-term memory models of water quality in inland water environments

Long short-term memory models of water quality in inland water environments

Multilingual Multi-Target Stance Recognition in Online Public Consultations

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