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The reader extends the Long\nShort-Term Memory architecture with a memory network in place of a single\nmemory cell. This enables adaptive memory usage during recurrence with neural\nattention, offering a way to weakly induce relations among tokens. The system\nis initially designed to process a single sequence but we also demonstrate how\nto integrate it with an encoder-decoder architecture. Experiments on language\nmodeling, sentiment analysis, and natural language inference show that our\nmodel matches or outperforms the state of the art.","url_abs":"http://arxiv.org/abs/1601.06733v7","url_pdf":"http://arxiv.org/pdf/1601.06733v7.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":"long-short-term-memory-networks-for-machine","repo_url":"https://github.com/JRC1995/Abstractive-Summarization","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"unanswered"}},{"paper_slug":"long-short-term-memory-networks-for-machine","repo_url":"https://github.com/muu4649/word2vec_attention","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok"}},{"paper_slug":"long-short-term-memory-networks-for-machine","repo_url":"https://github.com/oneil512/lstmn","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"decoder","task_name":"Decoder"},{"task_slug":"language-modeling","task_name":"Language Modeling"},{"task_slug":"language-modelling","task_name":"Language Modelling"},{"task_slug":"natural-language-inference","task_name":"Natural Language Inference"},{"task_slug":"reading-comprehension","task_name":"Reading Comprehension"},{"task_slug":"sentiment-analysis","task_name":"Sentiment Analysis"}],"methods":[{"method_slug":"memory-network","method_name":"Memory Network"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/natural-language-inference-on-snli","task":"Natural Language Inference","dataset":"SNLI","model":"450D LSTMN with deep attention fusion","rank_in_archive_order":60,"of":98,"metrics":{"% Test Accuracy":"86.3","% Train Accuracy":"88.5","Parameters":"3.4m"},"uses_additional_data":false},{"leaderboard":"/sota/natural-language-inference-on-snli","task":"Natural Language Inference","dataset":"SNLI","model":"300D LSTMN with deep attention fusion","rank_in_archive_order":68,"of":98,"metrics":{"% Test Accuracy":"85.7","% Train Accuracy":"87.3","Parameters":"1.7m"},"uses_additional_data":false}],"syntology":{"syntology_url":null,"atlas_url":"https://app.syntology.ai/?focus=1601.06733","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1601.06733"}},"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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