{"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/learning-to-generate-reviews-and-discovering","title":"Learning to Generate Reviews and Discovering Sentiment","arxiv_id":"1704.01444","date":"2017-04-05","proceeding":"ICLR 2018 1","authors":["Alec Radford","Rafal Jozefowicz","Ilya Sutskever"],"abstract":"We explore the properties of byte-level recurrent language models. When given\nsufficient amounts of capacity, training data, and compute time, the\nrepresentations learned by these models include disentangled features\ncorresponding to high-level concepts. Specifically, we find a single unit which\nperforms sentiment analysis. These representations, learned in an unsupervised\nmanner, achieve state of the art on the binary subset of the Stanford Sentiment\nTreebank. They are also very data efficient. When using only a handful of\nlabeled examples, our approach matches the performance of strong baselines\ntrained on full datasets. We also demonstrate the sentiment unit has a direct\ninfluence on the generative process of the model. Simply fixing its value to be\npositive or negative generates samples with the corresponding positive or\nnegative sentiment.","url_abs":"http://arxiv.org/abs/1704.01444v2","url_pdf":"http://arxiv.org/pdf/1704.01444v2.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":"learning-to-generate-reviews-and-discovering","repo_url":"https://github.com/faramarzmunshi/d2l-nlp","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"unanswered"}},{"paper_slug":"learning-to-generate-reviews-and-discovering","repo_url":"https://github.com/openai/generating-reviews-discovering-sentiment","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[{"task_slug":"sentiment-analysis","task_name":"Sentiment Analysis"},{"task_slug":"subjectivity-analysis","task_name":"Subjectivity Analysis"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/sentiment-analysis-on-sst-2-binary","task":"Sentiment Analysis","dataset":"SST-2 Binary classification","model":"bmLSTM","rank_in_archive_order":47,"of":87,"metrics":{"Accuracy":"91.8"},"uses_additional_data":false},{"leaderboard":"/sota/subjectivity-analysis-on-subj","task":"Subjectivity Analysis","dataset":"SUBJ","model":"Byte mLSTM","rank_in_archive_order":9,"of":19,"metrics":{"Accuracy":"94.60"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1704.01444","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1704.01444"}},"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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