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✨GAIC-2026✨

🙉 Generalizability of Argument Identification in Context (GAIC) — CLEF 2026

This repository contains the datasets for the Generalizability of Argument Identification in Context shared task at Touché @ CLEF 2026. Participants are asked to build models that classify whether a sentence (with context and metadata) is an Argument or No-Argument sentence across diverse sources.


💾 Dataset Overview

The table below lists the dataset folders in this repository under data/.

Dataset Folder Description Source License
ABSTRCT Argument mining dataset from academic abstracts. https://ecai2020.eu/papers/1470_paper CC BY-NC-SA 4.0
ACQUA Comparative sentences expressing preference or superiority (e.g. Matlab vs. Python) across multiple domains. https://aclanthology.org/W19-4516/ CC BY 4.0
AEC Sentences collected from discussions on the CreateDebate platform. https://aclanthology.org/W15-4631/ Approved by authors.
AFS Sentences drawn from online debate platforms such as ProCon and iDebate. https://aclanthology.org/W16-3636/ Approved by authors.
ARGUMINSCI Sentences originating from the Dr. Inventor scientific argumentation corpus. https://aclanthology.org/W18-5206/ Approved by authors.
FINARG Sentences extracted from financial earnings calls of publicly traded companies. https://aclanthology.org/2022.finnlp-1.22/ GNU GPL 3.0
IAM Sentences gathered from heterogeneous web sources. https://aclanthology.org/2022.acl-long.162/ Free license.
PE Sentences taken from student-written persuasive essays. https://aclanthology.org/J17-3005/ CC BY-NC-ND 4.0
SCIARK Sentences from scientific literature, including biomedical research articles. https://aclanthology.org/2021.argmining-1.10/ Free license.
USELEC Sentences from U.S. presidential election debates and related political discourse. https://aclanthology.org/P19-1463/ Free license.

☝️ Participation Instructions

  1. Read and follow the requirements for the task requirements on the Touché shared task page.
  2. Download the data and follow the further guidelines on the TIRA platform.

✌️ Notice

  1. The respective train/dev/test splits will be published sequentially in data/ as separate files (e.g., train.jsonl).
  2. The paths in train.jsonl etc. are relative to the data/ directory and point to the respective files (e.g., ./ABSTRCT/data/ABSTRCT-1.txt).

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