{"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/amore-upf-at-semeval-2018-task-4-bilstm-with","title":"AMORE-UPF at SemEval-2018 Task 4: BiLSTM with Entity Library","arxiv_id":"1805.05370","date":"2018-05-14","proceeding":"SEMEVAL 2018 6","authors":["Laura Aina","Carina Silberer","Ionut-Teodor Sorodoc","Matthijs Westera","Gemma Boleda"],"abstract":"This paper describes our winning contribution to SemEval 2018 Task 4:\nCharacter Identification on Multiparty Dialogues. It is a simple, standard\nmodel with one key innovation, an entity library. Our results show that this\ninnovation greatly facilitates the identification of infrequent characters.\nBecause of the generic nature of our model, this finding is potentially\nrelevant to any task that requires effective learning from sparse or unbalanced\ndata.","url_abs":"http://arxiv.org/abs/1805.05370v1","url_pdf":"http://arxiv.org/pdf/1805.05370v1.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":"amore-upf-at-semeval-2018-task-4-bilstm-with","repo_url":"https://github.com/amore-upf/semeval2018-task4","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}