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The Choice Of Plausible Alternatives (COPA) evaluation provides researchers with a tool for assessing progress in open-domain commonsense causal reasoning. COPA consists of 1000 questions, split equally into development and test sets of 500 questions each. Each question is composed of a premise and two alternatives, where the task is to select the alternative that more plausibly has a causal relation with the premise. The correct alternative is randomized so that the expected performance of randomly guessing is 50%. Source: Choice of Plausible Alternatives (COPA) Image Source: https://people.ict.usc.edu/~gordon/copa.html

The Cross-lingual Choice of Plausible Alternatives (XCOPA) dataset is a benchmark to evaluate the ability of machine learning models to transfer commonsense reasoning across languages. The dataset is the translation and reannotation of the English COPA (Roemmele et al. 2011) and covers 11 languages from 11 families and several areas around the globe. The dataset is challenging as it requires both the command of world knowledge and the ability to generalise to new languages. Source: https://github.com/cambridgeltl/xcopa

We provide the BCOPA-CE test set, which has balanced token distribution in the correct and wrong alternatives and increases the difficulty of being aware of cause and effect. ### construction 1. for each premise of the 500 samples in COPA-test set, we generate one event manually which is a plausible answer to the opposite question type of the original sample. 2. obtain 500 triplets of <*premise*, *cause*, *effect*> 3. construct 1000 samples by giving two different questions (cause or effect) to each triplet.

Semi-Structured Explanations for COPA (COPA-SSE) is a new crowdsourced dataset of 9,747 semi-structured, English common sense explanations for COPA questions. The explanations are formatted as a set of triple-like common sense statements with ConceptNet relations but freely written concepts. This semi-structured format strikes a balance between the high quality but low coverage of structured data and the lower quality but high coverage of free-form crowdsourcing. Each explanation also includes a set of human-given quality ratings. With their familiar format, the explanations are geared towards commonsense reasoners operating on knowledge graphs and serve as a starting point for ongoing work on improving such systems.

The Balanced Choice of Plausible Alternatives dataset is a benchmark for training machine learning models that are robust to superficial cues/spurious correlations. The dataset extends the COPA dataset(Roemmele et al. 2011) with mirrored instances that mitigate against token-level superficial cues in the original COPA answers. The superficial cues in the original COPA datasets result from an unbalanced token distribution between the correct and the incorrect answer choices, i.e., some tokens appear more in the correct choices than the incorrect ones. Balanced COPA equalizes the token distribution by adding mirrored instances with identical answer choices but different labels. The details about the creation of Balanced COPA and the implementation of the baselines are available in the paper.

The COPA-HR dataset (Choice of plausible alternatives in Croatian) is a translation of the English COPA dataset by following the XCOPA dataset translation methodology. The dataset consists of 1000 premises (My body cast a shadow over the grass), each given a question (What is the cause?), and two choices (The sun was rising; The grass was cut), with a label encoding which of the choices is more plausible given the annotator or translator (The sun was rising). Source: Choice of plausible alternatives dataset in Croatian COPA-HR

Visual Choice of Plausible Alternatives (VCOPA) is an evaluation dataset containing 380 VCOPA questions and over 1K images with various topics, which is amenable to automatic evaluation, and present the performance of baseline reasoning approaches as initial benchmarks for future systems. Source: Visual Choice of Plausible Alternatives: An Evaluation of Image-based Commonsense Causal Reasoning

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