{"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/a-neural-language-model-for-dynamically","title":"A Neural Language Model for Dynamically Representing the Meanings of Unknown Words and Entities in a Discourse","arxiv_id":"1709.01679","date":"2017-09-06","proceeding":"IJCNLP 2017 11","authors":["Sosuke Kobayashi","Naoaki Okazaki","Kentaro Inui"],"abstract":"This study addresses the problem of identifying the meaning of unknown words\nor entities in a discourse with respect to the word embedding approaches used\nin neural language models. We proposed a method for on-the-fly construction and\nexploitation of word embeddings in both the input and output layers of a neural\nmodel by tracking contexts. This extends the dynamic entity representation used\nin Kobayashi et al. (2016) and incorporates a copy mechanism proposed\nindependently by Gu et al. (2016) and Gulcehre et al. (2016). In addition, we\nconstruct a new task and dataset called Anonymized Language Modeling for\nevaluating the ability to capture word meanings while reading. Experiments\nconducted using our novel dataset show that the proposed variant of RNN\nlanguage model outperformed the baseline model. Furthermore, the experiments\nalso demonstrate that dynamic updates of an output layer help a model predict\nreappearing entities, whereas those of an input layer are effective to predict\nwords following reappearing entities.","url_abs":"http://arxiv.org/abs/1709.01679v2","url_pdf":"http://arxiv.org/pdf/1709.01679v2.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":"a-neural-language-model-for-dynamically","repo_url":"https://github.com/soskek/dynamic_neural_text_model","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"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":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}