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Home / Proceedings / Proceedings of the AAAI Conference on Artificial Intelligence, 36 / No. 10: AAAI-22 Technical Tracks 10

Few-Shot Cross-Lingual Stance Detection with Sentiment-Based Pre-training

February 1, 2023

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Authors

Momchil Hardalov

Checkstep Research Sofia University "St. Kliment Ohridski", Bulgaria


Arnav Arora

Checkstep Research University of Copenhagen, Denmark


Preslav Nakov

Checkstep Research Qatar Computing Research Institute, HBKU, Qatar


Isabelle Augenstein

Checkstep Research University of Copenhagen, Denmark


DOI:

10.1609/aaai.v36i10.21318


Abstract:

The goal of stance detection is to determine the viewpoint expressed in a piece of text towards a target. These viewpoints or contexts are often expressed in many different languages depending on the user and the platform, which can be a local news outlet, a social media platform, a news forum, etc. Most research on stance detection, however, has been limited to working with a single language and on a few limited targets, with little work on cross-lingual stance detection. Moreover, non-English sources of labelled data are often scarce and present additional challenges. Recently, large multilingual language models have substantially improved the performance on many non-English tasks, especially such with a limited number of examples. This highlights the importance of model pre-training and its ability to learn from few examples. In this paper, we present the most comprehensive study of cross-lingual stance detection to date: we experiment with 15 diverse datasets in 12 languages from 6 language families, and with 6 low-resource evaluation settings each. For our experiments, we build on pattern-exploiting training (PET), proposing the addition of a novel label encoder to simplify the verbalisation procedure. We further propose sentiment-based generation of stance data for pre-training, which shows sizeable improvement of more than 6% F1 absolute in few-shot learning settings compared to several strong baselines.

Topics: AAAI

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HOW TO CITE:

Momchil Hardalov||Arnav Arora||Preslav Nakov||Isabelle Augenstein Few-Shot Cross-Lingual Stance Detection with Sentiment-Based Pre-training Proceedings of the AAAI Conference on Artificial Intelligence (2022) 10729-10737.

Momchil Hardalov||Arnav Arora||Preslav Nakov||Isabelle Augenstein Few-Shot Cross-Lingual Stance Detection with Sentiment-Based Pre-training AAAI 2022, 10729-10737.

Momchil Hardalov||Arnav Arora||Preslav Nakov||Isabelle Augenstein (2022). Few-Shot Cross-Lingual Stance Detection with Sentiment-Based Pre-training. Proceedings of the AAAI Conference on Artificial Intelligence, 10729-10737.

Momchil Hardalov||Arnav Arora||Preslav Nakov||Isabelle Augenstein. Few-Shot Cross-Lingual Stance Detection with Sentiment-Based Pre-training. Proceedings of the AAAI Conference on Artificial Intelligence 2022 p.10729-10737.

Momchil Hardalov||Arnav Arora||Preslav Nakov||Isabelle Augenstein. 2022. Few-Shot Cross-Lingual Stance Detection with Sentiment-Based Pre-training. "Proceedings of the AAAI Conference on Artificial Intelligence". 10729-10737.

Momchil Hardalov||Arnav Arora||Preslav Nakov||Isabelle Augenstein. (2022) "Few-Shot Cross-Lingual Stance Detection with Sentiment-Based Pre-training", Proceedings of the AAAI Conference on Artificial Intelligence, p.10729-10737

Momchil Hardalov||Arnav Arora||Preslav Nakov||Isabelle Augenstein, "Few-Shot Cross-Lingual Stance Detection with Sentiment-Based Pre-training", AAAI, p.10729-10737, 2022.

Momchil Hardalov||Arnav Arora||Preslav Nakov||Isabelle Augenstein. "Few-Shot Cross-Lingual Stance Detection with Sentiment-Based Pre-training". Proceedings of the AAAI Conference on Artificial Intelligence, 2022, p.10729-10737.

Momchil Hardalov||Arnav Arora||Preslav Nakov||Isabelle Augenstein. "Few-Shot Cross-Lingual Stance Detection with Sentiment-Based Pre-training". Proceedings of the AAAI Conference on Artificial Intelligence, (2022): 10729-10737.

Momchil Hardalov||Arnav Arora||Preslav Nakov||Isabelle Augenstein. Few-Shot Cross-Lingual Stance Detection with Sentiment-Based Pre-training. AAAI[Internet]. 2022[cited 2023]; 10729-10737.


ISSN: 2374-3468


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