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Recurrent steps are\nused to perform local and global information exchange between words\nsimultaneously, rather than incremental reading of a sequence of words. Results\non various classification and sequence labelling benchmarks show that the\nproposed model has strong representation power, giving highly competitive\nperformances compared to stacked BiLSTM models with similar parameter numbers.","url_abs":"http://arxiv.org/abs/1805.02474v1","url_pdf":"http://arxiv.org/pdf/1805.02474v1.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":"sentence-state-lstm-for-text-representation","repo_url":"https://github.com/leuchine/S-LSTM","is_official":0,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null},{"paper_slug":"sentence-state-lstm-for-text-representation","repo_url":"https://github.com/LahiruSen/S-LSTM_Sinhala","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}}],"tasks":[{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"named-entity-recognition-ner","task_name":"Named Entity Recognition (NER)"},{"task_slug":"part-of-speech-tagging","task_name":"Part-Of-Speech Tagging"},{"task_slug":"sentence","task_name":"Sentence"},{"task_slug":"sentiment-analysis","task_name":"Sentiment Analysis"},{"task_slug":"text-classification","task_name":"Text Classification"}],"methods":[{"method_slug":"bilstm","method_name":"BiLSTM"},{"method_slug":"lstm","method_name":"LSTM"},{"method_slug":"sigmoid-activation","method_name":"Sigmoid Activation"},{"method_slug":"tanh-activation","method_name":"Tanh Activation"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/named-entity-recognition-ner-on-conll-2003","task":"Named Entity Recognition (NER)","dataset":"CoNLL 2003 (English)","model":"S-LSTM","rank_in_archive_order":59,"of":73,"metrics":{"F1":"91.57"},"uses_additional_data":false},{"leaderboard":"/sota/part-of-speech-tagging-on-penn-treebank","task":"Part-Of-Speech Tagging","dataset":"Penn Treebank","model":"S-LSTM","rank_in_archive_order":12,"of":20,"metrics":{"Accuracy":"97.55"},"uses_additional_data":false},{"leaderboard":"/sota/sentiment-analysis-on-imdb","task":"Sentiment Analysis","dataset":"IMDb","model":"S-LSTM","rank_in_archive_order":44,"of":49,"metrics":{"Accuracy":"87.15"},"uses_additional_data":true},{"leaderboard":"/sota/sentiment-analysis-on-mr","task":"Sentiment Analysis","dataset":"MR","model":"S-LSTM","rank_in_archive_order":16,"of":19,"metrics":{"Accuracy":"76.2"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1805.02474","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1805.02474"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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