{"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/sliced-recurrent-neural-networks","title":"Sliced Recurrent Neural Networks","arxiv_id":"1807.02291","date":"2018-07-06","proceeding":"COLING 2018 8","authors":["Zeping Yu","Gongshen Liu"],"abstract":"Recurrent neural networks have achieved great success in many NLP tasks.\nHowever, they have difficulty in parallelization because of the recurrent\nstructure, so it takes much time to train RNNs. In this paper, we introduce\nsliced recurrent neural networks (SRNNs), which could be parallelized by\nslicing the sequences into many subsequences. SRNNs have the ability to obtain\nhigh-level information through multiple layers with few extra parameters. We\nprove that the standard RNN is a special case of the SRNN when we use linear\nactivation functions. Without changing the recurrent units, SRNNs are 136 times\nas fast as standard RNNs and could be even faster when we train longer\nsequences. Experiments on six largescale sentiment analysis datasets show that\nSRNNs achieve better performance than standard RNNs.","url_abs":"http://arxiv.org/abs/1807.02291v1","url_pdf":"http://arxiv.org/pdf/1807.02291v1.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":"sliced-recurrent-neural-networks","repo_url":"https://github.com/zepingyu0512/srnn","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"tf","reach":{"status":"ok"}},{"paper_slug":"sliced-recurrent-neural-networks","repo_url":"https://github.com/MingyuLi19910814/Sliced-Recurrent-Neural-Network-Tensorflow2","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok"}},{"paper_slug":"sliced-recurrent-neural-networks","repo_url":"https://github.com/archanasgujar/DashBoard","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}}],"tasks":[{"task_slug":"sentiment-analysis","task_name":"Sentiment Analysis"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/sentiment-analysis-on-amazon-review-full","task":"Sentiment Analysis","dataset":"Amazon Review Full","model":"SRNN","rank_in_archive_order":6,"of":9,"metrics":{"Accuracy":"61.65"},"uses_additional_data":false},{"leaderboard":"/sota/sentiment-analysis-on-amazon-review-polarity","task":"Sentiment Analysis","dataset":"Amazon Review Polarity","model":"SRNN","rank_in_archive_order":6,"of":9,"metrics":{"Accuracy":"95.26"},"uses_additional_data":false},{"leaderboard":"/sota/sentiment-analysis-on-yelp-binary","task":"Sentiment Analysis","dataset":"Yelp Binary classification","model":"SRNN","rank_in_archive_order":15,"of":20,"metrics":{"Error":"3.96"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1807.02291","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}