{"url":"/dataset/storycloze","name":"StoryCloze","full_name":null,"description_markdown":"Representation and learning of commonsense knowledge is one of the foundational problems in the quest to enable deep language understanding. This issue is particularly challenging for understanding casual and correlational relationships between events. While this topic has received a lot of interest in the NLP community, research has been hindered by the lack of a proper evaluation framework. This paper attempts to address this problem with a new framework for evaluating story understanding and script learning: the 'Story Cloze Test'. This test requires a system to choose the correct ending to a four-sentence story. We created a new corpus of ~50k five-sentence commonsense stories, ROCStories, to enable this evaluation. This corpus is unique in two ways: (1) it captures a rich set of causal and temporal commonsense relations between daily events, and (2) it is a high quality collection of everyday life stories that can also be used for story generation. Experimental evaluation shows that a host of baselines and state-of-the-art models based on shallow language understanding struggle to achieve a high score on the Story Cloze Test. We discuss these implications for script and story learning, and offer suggestions for deeper language understanding.","description_withheld":null,"homepage":"https://huggingface.co/datasets/story_cloze","introduced_date":"2016-04-06","introduced_date_note":null,"introduced_by":{"paper":"/paper/a-corpus-and-evaluation-framework-for-deeper","title":"A Corpus and Evaluation Framework for Deeper Understanding of Commonsense Stories","first_author":"Nasrin Mostafazadeh","url":null},"license":null,"modalities":[],"tasks":[{"name":"Question Answering","url":"/task/question-answering","datasets_with_task":"/datasets/task/question-answering"}],"languages":[],"variants":["Story Cloze Test","StoryCloze"],"data_loaders":[],"num_papers_in_archive":51,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/question-answering-on-storycloze","task":"Question Answering","dataset_variant":"StoryCloze","rows":23,"metrics":["Accuracy"],"first_row_in_archive_order":{"model":"BLOOMZ","paper":"/paper/crosslingual-generalization-through-multitask","metrics":{"Accuracy":"96.3"},"code_links":[{"title":"bigscience-workshop/xmtf","url":"https://github.com/bigscience-workshop/xmtf"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/the-cot-collection-improving-zero-shot-and","title":"The CoT Collection: Improving Zero-shot and Few-shot Learning of Language Models via Chain-of-Thought Fine-Tuning","date":"2023-05-23","rows_on_this_dataset":1,"code_links":2,"syntology":null},{"paper":"/paper/exploring-the-benefits-of-training-expert","title":"Exploring the Benefits of Training Expert Language Models over Instruction Tuning","date":"2023-02-07","rows_on_this_dataset":1,"code_links":2,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":3,"samples_ran":3,"samples_unverified":0,"pointer_only_for_licence":3,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/massive-language-models-can-be-accurately","title":"SparseGPT: Massive Language Models Can Be Accurately Pruned in One-Shot","date":"2023-01-02","rows_on_this_dataset":5,"code_links":6,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":12,"samples_ran":2,"samples_unverified":10,"pointer_only_for_licence":9,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/crosslingual-generalization-through-multitask","title":"Crosslingual Generalization through Multitask Finetuning","date":"2022-11-03","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":4,"samples_ran":1,"samples_unverified":3,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/knowledge-in-context-towards-knowledgeable","title":"Knowledge-in-Context: Towards Knowledgeable Semi-Parametric Language Models","date":"2022-10-28","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/guess-the-instruction-making-language-models","title":"Guess the Instruction! Flipped Learning Makes Language Models Stronger Zero-Shot Learners","date":"2022-10-06","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/efficient-language-modeling-with-sparse-all","title":"Efficient Language Modeling with Sparse all-MLP","date":"2022-03-14","rows_on_this_dataset":5,"code_links":0,"syntology":null},{"paper":"/paper/finetuned-language-models-are-zero-shot","title":"Finetuned Language Models Are Zero-Shot Learners","date":"2021-09-03","rows_on_this_dataset":2,"code_links":8,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":1,"samples_ran":0,"samples_unverified":1,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/language-models-are-few-shot-learners","title":"Language Models are Few-Shot Learners","date":"2020-05-28","rows_on_this_dataset":1,"code_links":67,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":65,"samples_ran":15,"samples_unverified":50,"pointer_only_for_licence":4,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/improving-machine-reading-comprehension-with","title":"Improving Machine Reading Comprehension with General Reading Strategies","date":"2018-10-31","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/improving-language-understanding-by","title":"Improving Language Understanding by Generative Pre-Training","date":"2018-06-11","rows_on_this_dataset":1,"code_links":13,"syntology":null},{"paper":"/paper/a-simple-and-effective-approach-to-the-story","title":"A Simple and Effective Approach to the Story Cloze Test","date":"2018-03-15","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/story-comprehension-for-predicting-what","title":"Story Comprehension for Predicting What Happens Next","date":"2017-09-01","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/unimelb-at-semeval-2016-tasks-4a-and-4b-an","title":"UNIMELB at SemEval-2016 Tasks 4A and 4B: An Ensemble of Neural Networks and a Word2Vec Based Model for Sentiment Classification","date":"2016-06-01","rows_on_this_dataset":1,"code_links":1,"syntology":null}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":5,"samples_harvested":85,"samples_ran":21,"samples_unverified":64,"pointer_only_for_licence":16,"papers_with_no_sample_that_ran":1,"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."}