{"url":"/dataset/ecqa","name":"ECQA","full_name":"Explanations for CommonsenseQA","description_markdown":"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](https://github.com/dair-iitd/ECQA-Dataset) and [code](https://github.com/dair-iitd/ECQA) are also made available through our AIHN collaboration partner institute IIT Delhi. You can download the full paper from [here](https://aclanthology.org/2021.acl-long.238/).\r\n\r\nNote that these annotations are provided for the questions of the CommonsenseQA data ([https://www.tau-nlp.org/commonsenseqa](https://www.tau-nlp.org/commonsenseqa)): arXiv:1811.00937 [cs.CL] (or arXiv:1811.00937v2 [cs.CL] for this version).\r\n\r\n### Citations\r\nPlease consider citing this paper as follows:\r\n```\r\n@inproceedings{aggarwaletal2021ecqa,\r\n  title={{E}xplanations for {C}ommonsense{QA}: {N}ew {D}ataset and {M}odels},\r\n  author={Shourya Aggarwal and Divyanshu Mandowara and Vishwajeet Agrawal and Dinesh Khandelwal and Parag Singla and Dinesh Garg},\r\n  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)}\",\r\n  Pages = 3050–3065,\r\n  year = \"2021\",\r\n  publisher = \"Association for Computational Linguistics\"\r\n}\r\n```","description_withheld":null,"homepage":"https://github.com/IBM/ecqa","introduced_date":"2021-07-15","introduced_date_note":null,"introduced_by":{"paper":"/paper/explanations-for-commonsenseqa-new-dataset","title":"Explanations for CommonsenseQA: New Dataset and Models","first_author":"Shourya Aggarwal","url":null},"license":{"name":"CDLA Sharing 1.0","url":"https://github.com/Community-Data-License-Agreements/Releases"},"modalities":[],"tasks":[],"languages":[],"variants":["ECQA"],"data_loaders":[],"num_papers_in_archive":50,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[],"papers_with_a_benchmark_row":[],"syntology_totals":{"read_at":"2026-09-25T09:33:49+00:00","papers_with_samples":0,"samples_harvested":0,"samples_ran":0,"samples_unverified":0,"pointer_only_for_licence":0,"papers_with_no_sample_that_ran":0,"note":"the per-paper counts above, summed; not a rate"},"papers_note":"The archive never published its papers-using-dataset list; these are papers with a leaderboard row on this dataset's benchmarks."}