{"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/compressing-neural-language-models-by-sparse","title":"Compressing Neural Language Models by Sparse Word Representations","arxiv_id":"1610.03950","date":"2016-10-13","proceeding":"ACL 2016 8","authors":["Yunchuan Chen","Lili Mou","Yan Xu","Ge Li","Zhi Jin"],"abstract":"Neural networks are among the state-of-the-art techniques for language\nmodeling. Existing neural language models typically map discrete words to\ndistributed, dense vector representations. After information processing of the\npreceding context words by hidden layers, an output layer estimates the\nprobability of the next word. Such approaches are time- and memory-intensive\nbecause of the large numbers of parameters for word embeddings and the output\nlayer. In this paper, we propose to compress neural language models by sparse\nword representations. In the experiments, the number of parameters in our model\nincreases very slowly with the growth of the vocabulary size, which is almost\nimperceptible. Moreover, our approach not only reduces the parameter space to a\nlarge extent, but also improves the performance in terms of the perplexity\nmeasure.","url_abs":"http://arxiv.org/abs/1610.03950v1","url_pdf":"http://arxiv.org/pdf/1610.03950v1.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":"compressing-neural-language-models-by-sparse","repo_url":"https://github.com/chenych11/lm","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"language-modeling","task_name":"Language Modeling"},{"task_slug":"language-modelling","task_name":"Language Modelling"},{"task_slug":"word-embeddings","task_name":"Word Embeddings"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1610.03950","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1610.03950"}},"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. Samples come from repositories linked to the paper, official or community; repo_kind says which.","repos":[{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/chenych11/lm","reach":null}],"summary":{"ran_draft_wrong":1},"by_repo_kind":{"official":{"samples":1,"ran":1,"repositories":1}},"repo_kind_vocabulary":{"official":"The archive marks this repository official for the paper","named_in_paper":"The archive records that the paper mentions this repository; it is not marked official","listed":"In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper","found_in_text":"Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted","community":"Not in the archive's code links for this paper; a community repository Syntology harvested"},"n_pointer_only_for_licence":1,"samples":[{"code_sha256_prefix":"1abf854ea579bf24","entry":"negative_sampleLBLV2","repo":"chenych11/lm","repo_kind":"official","path":"real/models.py","file_url":"https://github.com/chenych11/lm/blob/HEAD/real/models.py","link_basis":"first_harvest_node","language":"python","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"1abf854ea579bf24"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}