# Multilingual Target-Stance Extraction

arXiv: https://arxiv.org/abs/2510.22334 · HTML: https://mlsanity.com/paper/2510.22334/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 21 of its 46 references that are in the map.
- First posted on arXiv 25 Oct 2025.

## Builds on (most cited first, 21 of 21)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [mT5: A massively multilingual pre-trained text-to-text transformer](https://mlsanity.com/paper/2010.11934/) (2020)
- [Enriching Word Vectors with Subword Information](https://mlsanity.com/paper/1607.04606/) (2016)
- [Beyond English-Centric Multilingual Machine Translation](https://mlsanity.com/paper/2010.11125/) (2020)
- [BERTweet: A pre-trained language model for English Tweets](https://mlsanity.com/paper/2005.10200/) (2020)
- [Zero-Shot Stance Detection: A Dataset and Model using Generalized Topic Representations](https://mlsanity.com/paper/2010.03640/) (2020)
- [Stance Detection with Bidirectional Conditional Encoding](https://mlsanity.com/paper/1606.05464/) (2016)
- [Will-They-Won't-They: A Very Large Dataset for Stance Detection on Twitter](https://mlsanity.com/paper/2005.00388/) (2020)
- [X-Stance: A Multilingual Multi-Target Dataset for Stance Detection](https://mlsanity.com/paper/2003.08385/) (2020)
- [Cross-Target Stance Classification with Self-Attention Networks](https://mlsanity.com/paper/1805.06593/) (2018)
- [KPTimes: A Large-Scale Dataset for Keyphrase Generation on News Documents](https://mlsanity.com/paper/1911.12559/) (2019)
- [Adversarial Learning for Zero-Shot Stance Detection on Social Media](https://mlsanity.com/paper/2105.06603/) (2021)
- [Few-Shot Cross-Lingual Stance Detection with Sentiment-Based Pre-Training](https://mlsanity.com/paper/2109.06050/) (2021)
- [MITRE at SemEval-2016 Task 6: Transfer Learning for Stance Detection](https://mlsanity.com/paper/1606.03784/) (2016)
- [Infusing Knowledge from Wikipedia to Enhance Stance Detection](https://mlsanity.com/paper/2204.03839/) (2022)
- [Multi-modal Stance Detection: New Datasets and Model](https://mlsanity.com/paper/2402.14298/) (2024)
- [Multilingual Stance Detection: The Catalonia Independence Corpus](https://mlsanity.com/paper/2004.00050/) (2020)
- [Exploiting Sentiment and Common Sense for Zero-shot Stance Detection](https://mlsanity.com/paper/2208.08797/) (2022)
- [Automated stance detection in complex topics and small languages: the challenging case of immigration in polarizing news media](https://mlsanity.com/paper/2305.13047/) (2023)
- [EUROPA: A Legal Multilingual Keyphrase Generation Dataset](https://mlsanity.com/paper/2403.00252/) (2024)

## Abstract (arXiv)

Social media enables data-driven analysis of public opinion on contested issues. Target-Stance Extraction (TSE) is the task of identifying the target discussed in a document and the document's stance towards that target. Many works classify stance towards a given target in a multilingual setting, but all prior work in TSE is English-only. This work introduces the first multilingual TSE benchmark, spanning Catalan, Estonian, French, Italian, Mandarin, and Spanish corpora. It manages to extend the original TSE pipeline to a multilingual setting without requiring separate models for each language. Our model pipeline achieves a modest F1 score of 12.78, underscoring the increased difficulty of the multilingual task relative to English-only setups and highlighting target prediction as the primary bottleneck. We are also the first to demonstrate the sensitivity of TSE's F1 score to different target verbalizations. Together these serve as a much-needed baseline for resources, algorithms, and evaluation criteria in multilingual TSE.