{"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/domain-agnostic-real-valued-specificity","title":"Domain Agnostic Real-Valued Specificity Prediction","arxiv_id":"1811.05085","date":"2018-11-13","proceeding":null,"authors":["Wei-Jen Ko","Greg Durrett","Junyi Jessy Li"],"abstract":"Sentence specificity quantifies the level of detail in a sentence,\ncharacterizing the organization of information in discourse. While this\ninformation is useful for many downstream applications, specificity prediction\nsystems predict very coarse labels (binary or ternary) and are trained on and\ntailored toward specific domains (e.g., news). The goal of this work is to\ngeneralize specificity prediction to domains where no labeled data is available\nand output more nuanced real-valued specificity ratings.\n  We present an unsupervised domain adaptation system for sentence specificity\nprediction, specifically designed to output real-valued estimates from binary\ntraining labels. To calibrate the values of these predictions appropriately, we\nregularize the posterior distribution of the labels towards a reference\ndistribution. We show that our framework generalizes well to three different\ndomains with 50%~68% mean absolute error reduction than the current\nstate-of-the-art system trained for news sentence specificity. We also\ndemonstrate the potential of our work in improving the quality and\ninformativeness of dialogue generation systems.","url_abs":"http://arxiv.org/abs/1811.05085v2","url_pdf":"http://arxiv.org/pdf/1811.05085v2.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":"domain-agnostic-real-valued-specificity","repo_url":"https://github.com/wjko2/Domain-Agnostic-Sentence-Specificity-Prediction","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null},{"paper_slug":"domain-agnostic-real-valued-specificity","repo_url":"https://github.com/jjessyli/speciteller","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"dialogue-generation","task_name":"Dialogue Generation"},{"task_slug":"domain-adaptation","task_name":"Domain Adaptation"},{"task_slug":"informativeness","task_name":"Informativeness"},{"task_slug":"prediction","task_name":"Prediction"},{"task_slug":"sentence","task_name":"Sentence"},{"task_slug":"specificity","task_name":"Specificity"},{"task_slug":"unsupervised-domain-adaptation","task_name":"Unsupervised Domain Adaptation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1811.05085","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}