{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/recognizing-arrow-of-time-in-the-short","title":"Recognizing Arrow Of Time In The Short Stories","arxiv_id":"1903.10548","date":"2019-03-25","proceeding":"WS 2019 8","authors":["Fahimeh Hosseini","Hosein Fooladi","Mohammad Reza Samsami"],"abstract":"Recognizing arrow of time in short stories is a challenging task. i.e., given\nonly two paragraphs, determining which comes first and which comes next is a\ndifficult task even for humans. In this paper, we have collected and curated a\nnovel dataset for tackling this challenging task. We have shown that a\npre-trained BERT architecture achieves reasonable accuracy on the task, and\noutperforms RNN-based architectures.","url_abs":"http://arxiv.org/abs/1903.10548v1","url_pdf":"http://arxiv.org/pdf/1903.10548v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"recognizing-arrow-of-time-in-the-short","repo_url":"https://github.com/ShenakhtPajouh/transposition-simple","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"tf","reach":null}],"tasks":[],"methods":[{"method_slug":"adam","method_name":"Adam"},{"method_slug":"attention","method_name":"Attention"},{"method_slug":"attention-dropout","method_name":"Attention Dropout"},{"method_slug":"bert","method_name":"BERT"},{"method_slug":"dense-connections","method_name":"Dense Connections"},{"method_slug":"dropout","method_name":"Dropout"},{"method_slug":"layer-normalization","method_name":"Layer Normalization"},{"method_slug":"linear-layer","method_name":"Linear Layer"},{"method_slug":"linear-warmup-with-linear-decay","method_name":"Linear Warmup With Linear Decay"},{"method_slug":"multi-head-attention","method_name":"Multi-Head Attention"},{"method_slug":"residual-connection","method_name":"Residual Connection"},{"method_slug":"softmax","method_name":"Softmax"},{"method_slug":"weight-decay","method_name":"Weight Decay"},{"method_slug":"wordpiece","method_name":"WordPiece"}],"datasets_introduced":[{"slug":"paragraphordreing","name":"ParagraphOrdreing","full_name":""}],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}