{"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/spine-sparse-interpretable-neural-embeddings","title":"SPINE: SParse Interpretable Neural Embeddings","arxiv_id":"1711.08792","date":"2017-11-23","proceeding":null,"authors":["Anant Subramanian","Danish Pruthi","Harsh Jhamtani","Taylor Berg-Kirkpatrick","Eduard Hovy"],"abstract":"Prediction without justification has limited utility. Much of the success of\nneural models can be attributed to their ability to learn rich, dense and\nexpressive representations. While these representations capture the underlying\ncomplexity and latent trends in the data, they are far from being\ninterpretable. We propose a novel variant of denoising k-sparse autoencoders\nthat generates highly efficient and interpretable distributed word\nrepresentations (word embeddings), beginning with existing word representations\nfrom state-of-the-art methods like GloVe and word2vec. Through large scale\nhuman evaluation, we report that our resulting word embedddings are much more\ninterpretable than the original GloVe and word2vec embeddings. Moreover, our\nembeddings outperform existing popular word embeddings on a diverse suite of\nbenchmark downstream tasks.","url_abs":"http://arxiv.org/abs/1711.08792v1","url_pdf":"http://arxiv.org/pdf/1711.08792v1.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":"spine-sparse-interpretable-neural-embeddings","repo_url":"https://github.com/harsh19/SPINE","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"unanswered"}},{"paper_slug":"spine-sparse-interpretable-neural-embeddings","repo_url":"https://github.com/jacobdanovitch/spine","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"denoising","task_name":"Denoising"},{"task_slug":"word-embeddings","task_name":"Word Embeddings"}],"methods":[{"method_slug":"glove","method_name":"GloVe"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1711.08792","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1711.08792"}},"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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