REVIEW 2 cited by
X-Stance: A Multilingual Multi-Target Dataset for Stance Detection
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
X-Stance: A Multilingual Multi-Target Dataset for Stance Detection
read the original abstract
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.
Forward citations
Cited by 2 Pith papers
-
SemEval-2026 Task 3: Dimensional Aspect-Based Sentiment Analysis (DimABSA)
The paper introduces the DimABSA shared task for SemEval-2026 that reformulates aspect-based sentiment analysis and stance detection as valence-arousal regression problems with subtasks for regression, triplet, and qu...
-
Rationale-Guided Knowledge Distillation for Cross-Lingual Stance Detection
Distilling LLM-generated reasoning rationales into mBERT via dual-path contrastive distillation improves cross-lingual stance detection by 1–3% accuracy on three benchmarks.
discussion (0)
Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.