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). Source: X-Stance: A Multilingual Multi-Target Dataset for Stance Detection

The Cornell eRulemaking Corpus – CDCP is an argument mining corpus annotated with argumentative structure information capturing the evaluability of arguments. The corpus consists of 731 user comments on Consumer Debt Collection Practices (CDCP) rule by the Consumer Financial Protection Bureau (CFPB); the resulting dataset contains 4931 elementary unit and 1221 support relation annotations. It is a resource for building argument mining systems that can not only extract arguments from unstructured text, but also identify what additional information is necessary for readers to understand and evaluate a given argument. Immediate applications include providing real-time feedback to commenters, specifying which types of support for which propositions can be added to construct better-formed arguments.

The UKP Argument Annotated Essays corpus consists of argument annotated persuasive essays including annotations of argument components and argumentative relations. Source: https://www.informatik.tu-darmstadt.de/ukp/research_6/data/argumentation_mining_1/argument_annotated_essays/index.en.jsp Image Source: https://www.aclweb.org/anthology/C14-1142.pdf

DebateSum consists of 187328 debate documents, arguments (also can be thought of as abstractive summaries, or queries), word-level extractive summaries, citations, and associated metadata organized by topic-year. This data is ready for analysis by NLP systems. Source: https://github.com/Hellisotherpeople/DebateSum

Data set covering a set of debatable topics, where for each topic and stance, a set of triplets of the form <argument, KP, label> is provided. The data set is based on the ArgKP data set, which contains arguments contributed by the crowd on 28 debatable topics, split by their stance towards the topic, and KPs written by an expert for those topics. Crowd annotations were collected to determine whether a KP represents an argument, i.e., is a match for an argument. The arguments in ArgKP are a subset of the [IBM-ArgQ-Rank-30kArgs data set](The arguments in ArgKP are a subset of the IBM-ArgQ-Rank-30kArgs data set). For a test set, we extended ArgKP, adding three new debatable topics, that were also not part of IBM-ArgQ-Rank-30kArgs. The test set was collected specifically for KPA-2021, and was carefully designed to be similar in various aspects to the training data 2 . For each topic, crowd sourced arguments were collected, expert KPs generated, and match/no match annotations for argument/KP pairs obtained, resulting in a data set compatible with the ArgKP format. Arguments collection strictly adhered to the guidelines, quality measures, and post processing used for the collection of arguments in IBM-ArgQ-Rank-30kArgs, while the generation of expert KPs, collection of match annotations, and final data set creation strictly adhered to the manner in which ArgKP was created. Source: [Overview of the 2021 Key Point Analysis Shared Task](Overview of the 2021 Key Point Analysis Shared Task)

The Few-Shot Object Learning (FewSOL) dataset can be used for object recognition with a few images per object. It contains 336 real-world objects with 9 RGB-D images per object from different views. Object segmentation masks, object poses and object attributes are provided. In addition, synthetic images generated using 330 3D object models are used to augment the dataset. FewSOL dataset can be used to study a set of few-shot object recognition problems such as classification, detection and segmentation, shape reconstruction, pose estimation, keypoint correspondences and attribute recognition. Motivation: If robots can recognize objects from a few exemplar images, it is possible to scale up the number of objects a robot can recognize because collecting a few images per object is a much easier process compared to building a 3D model of an object. In addition, models trained in the meta-learning setting can generalize to new objects without re-training.

Review-Rebuttal (RR) dataset is introduced to facilitate the study of argument pair extraction in the peer review and rebuttal domain.

We introduce a large and comprehensive dataset to facilitate the study of several essential AM tasks in the debating system. In our work, we first review the existing subtasks (claim extraction, stance classification, evidence extraction), and then propose two integrated argument mining tasks: claim extraction with stance classification (CESC) and claim-evidence pair extraction (CEPE).

A new synthetic, multi-purpose dataset - called ENRICH - for testing photogrammetric and computer vision algorithms. Compared to existing datasets, ENRICH offers higher resolution images also rendered with different lighting conditions, camera orientation, scales, and field of view. Specifically, ENRICH is composed of three sub-datasets: ENRICH-Aerial, ENRICH-Square, and ENRICH-Statue, each exhibiting different characteristics. The proposed dataset is useful for several photogrammetry and computer vision-related tasks, such as the evaluation of hand-crafted and deep learning-based local features, effects of ground control points (GCPs) configuration on the 3D accuracy, and monocular depth estimation. Each zip file in the root is relative to a specific dataset: - ENRICH-Aerial, is an aerial image block of the city of Launceston, Australia. The acquisition is performed by simulating a typical oblique aerial camera with five views (nadir and four oblique views). - ENRICH-Square, is a ground-level dataset of a square captured by four cameras, each one moving on a different path with different focal length, orientation, and lighting conditions. - ENRICH-Statue, is a ground-level dataset portraying a statue (placed in the center of the ENRICH-Square scene), acquired using four cameras moving on different paths with different focal lengths, orientations, and lighting conditions. Be sure to check the README file in the dataset root for information on folder structure and file contents. Please refer to the related paper (https://doi.org/10.1016/j.isprsjprs.2023.03.002) for information about the generation method and the purpose of ENRICH.

Binary labels for Validity and Novelty respectively are given for each Conclusion.

Freely licensed dataset with warrants for 2k authentic arguments from news comments. On this basis, we present a new challenging task, the argument reasoning comprehension task. Given an argument with a claim and a premise, the goal is to choose the correct implicit warrant from two options. Both warrants are plausible and lexically close, but lead to contradicting claims.

Corpus for argument mining in legal documents, composed of 40 decisions of the Court of Justice of the European Union on matters of fiscal state aid

We introduce OpenDebateEvidence, a comprehensive dataset for argument mining and summarization sourced from the American Competitive Debate community. This dataset includes over 3.5 million documents with rich metadata, making it one of the most extensive collections of debate evidence. OpenDebateEvidence captures the complexity of arguments in high school and college debates, pro- viding valuable resources for training and evaluation. Our extensive experiments demonstrate the efficacy of fine-tuning state-of-the-art large language models for argumentative abstractive summarization across various methods, models, and datasets. By providing this comprehensive resource, we aim to advance com- putational argumentation and support practical applications for debaters, edu- cators, and researchers. OpenDebateEvidence is publicly available to support further research and innovation in computational argumentation. Access it here: https://huggingface.co/datasets/Yusuf5/OpenCaselist

Press Briefing Claim Dataset The dataset contains a total of 53 press briefings from a time span of over four years (2017-2021). While, on average, one press briefing per month is held, the distribution is highly skewed towards recent years. The Press briefings can be categorized into five main thematic categories: Climate, Energy and Mobility, Medicine, Technology and Science. The press briefings are unevenly distributed among the five categories with a major focus on medical press briefings, which also reflects the COVID-19 pandemic. In total, 177 speakers, excluding the hosts, were detected in the dataset. Most of the time the speakers are invited experts. Excluding the top 10 percentile press briefings as outliers, a press briefing had four guests on average. The press briefings consist of 3066 passages, 1719 from guests and journalists and 1294 from hosts. Regarding the sentences, 25040 sentences were collected in total, 5955 from hosts and 18942 from guests.

Validity and Novelty are determined in a comparative setting between two conclusions at a time. For Validity and Novelty possible labels are "Conclusion 1 is better", "tie" and "Conclusion 2 is better", for Validity and Novelty respectively.

With the goal of reasoning on the financial textual data, we present a novel dataset for annotating arguments, their components, and relations in the transcripts of earnings conference calls (ECCs).