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The CommonsenseQA is a dataset for commonsense question answering task. The dataset consists of 12,247 questions with 5 choices each. The dataset was generated by Amazon Mechanical Turk workers in the following process (an example is provided in parentheses): 1. a crowd worker observes a source concept from ConceptNet (“River”) and three target concepts (“Waterfall”, “Bridge”, “Valley”) that are all related by the same ConceptNet relation (“AtLocation”), 2. the worker authors three questions, one per target concept, such that only that particular target concept is the answer, while the other two distractor concepts are not, (“Where on a river can you hold a cup upright to catch water on a sunny day?”, “Where can I stand on a river to see water falling without getting wet?”, “I’m crossing the river, my feet are wet but my body is dry, where am I?”) 3. for each question, another worker chooses one additional distractor from Concept Net (“pebble”, “stream”, “bank”), and the author another distractor (“mountain”, “bottom”, “island”) manually. Source: CommonsenseQA: A Question Answering Challenge Targeting Commonsense Knowledge Image Source: CommonsenseQA: A Question Answering Challenge Targeting Commonsense Knowledge

This repository contains the publicly released dataset, code, and models for the Explanations for CommonsenseQA paper presented at ACL-IJCNLP 2021. Directories data and code inside the root folder contain dataset and code, respectively. The same data and code are also made available through our AIHN collaboration partner institute IIT Delhi. You can download the full paper from here. Note that these annotations are provided for the questions of the CommonsenseQA data (https://www.tau-nlp.org/commonsenseqa): arXiv:1811.00937 [cs.CL] (or arXiv:1811.00937v2 [cs.CL] for this version). ### Citations Please consider citing this paper as follows: @inproceedings{aggarwaletal2021ecqa, title={{E}xplanations for {C}ommonsense{QA}: {N}ew {D}ataset and {M}odels}, author={Shourya Aggarwal and Divyanshu Mandowara and Vishwajeet Agrawal and Dinesh Khandelwal and Parag Singla and Dinesh Garg}, booktitle="Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)}", Pages = 3050–3065, year = "2021", publisher = "Association for Computational Linguistics" }