{"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/frustratingly-easy-meta-embedding-computing","title":"Frustratingly Easy Meta-Embedding -- Computing Meta-Embeddings by Averaging Source Word Embeddings","arxiv_id":"1804.05262","date":"2018-04-14","proceeding":"NAACL 2018 6","authors":["Joshua Coates","Danushka Bollegala"],"abstract":"Creating accurate meta-embeddings from pre-trained source embeddings has\nreceived attention lately. Methods based on global and locally-linear\ntransformation and concatenation have shown to produce accurate\nmeta-embeddings. In this paper, we show that the arithmetic mean of two\ndistinct word embedding sets yields a performant meta-embedding that is\ncomparable or better than more complex meta-embedding learning methods. The\nresult seems counter-intuitive given that vector spaces in different source\nembeddings are not comparable and cannot be simply averaged. We give insight\ninto why averaging can still produce accurate meta-embedding despite the\nincomparability of the source vector spaces.","url_abs":"http://arxiv.org/abs/1804.05262v1","url_pdf":"http://arxiv.org/pdf/1804.05262v1.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":"frustratingly-easy-meta-embedding-computing","repo_url":"https://github.com/Shujian2015/meta-embedding-paper-list","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"word-embeddings","task_name":"Word Embeddings"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1804.05262","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}