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X-Stance: A Multilingual Multi-Target Dataset for Stance Detection

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arxiv 2003.08385 v2 pith:BH4RAYDR submitted 2020-03-18 cs.CL

Jannis Vamvas , Rico Sennrich This is my paper · ORCID
classification cs.CL
keywords detectionstancedatasetissuescommentscross-lingualmultilingualtarget
verification ladder T0 review T1 audit T2 compute T3 formal
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We extract a large-scale stance detection dataset from comments written by candidates of elections in Switzerland. The dataset consists of German, French and Italian text, allowing for a cross-lingual evaluation of stance detection. It contains 67 000 comments on more than 150 political issues (targets). Unlike stance detection models that have specific target issues, we use the dataset to train a single model on all the issues. To make learning across targets possible, we prepend to each instance a natural question that represents the target (e.g. "Do you support X?"). Baseline results from multilingual BERT show that zero-shot cross-lingual and cross-target transfer of stance detection is moderately successful with this approach.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. ArgInstruct: Specialized Instruction Fine-Tuning for Computational Argumentation

    cs.CL 2025-05 conditional novelty 7.0 of 10

    Fine-tuning a Gemma model on a mix of seed, generated, and general instruction data (ArgInstruct) improves zero-shot performance on unseen computational argumentation tasks while preserving general NLP performance.

  2. Misleading through Inconsistency: A Benchmark for Political Inconsistencies Detection

    cs.CL 2025-05 conditional novelty 7.0 of 10

    A new 698-pair human-annotated benchmark and inconsistency typology for political language, with LLMs roughly matching individual annotators on coarse detection but not on fine-grained subtypes.

  3. Rationale-Guided Knowledge Distillation for Cross-Lingual Stance Detection

    cs.CL 2026-07 conditional novelty 5.0 of 10

    Distilling LLM-generated reasoning rationales into mBERT via dual-path contrastive distillation improves cross-lingual stance detection by 1–3% accuracy on three benchmarks.

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