{"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/finding-convincing-arguments-using-scalable","title":"Finding Convincing Arguments Using Scalable Bayesian Preference Learning","arxiv_id":"1806.02418","date":"2018-06-06","proceeding":"TACL 2018 1","authors":["Edwin Simpson","Iryna Gurevych"],"abstract":"We introduce a scalable Bayesian preference learning method for identifying\nconvincing arguments in the absence of gold-standard rat- ings or rankings. In\ncontrast to previous work, we avoid the need for separate methods to perform\nquality control on training data, predict rankings and perform pairwise\nclassification. Bayesian approaches are an effective solution when faced with\nsparse or noisy training data, but have not previously been used to identify\nconvincing arguments. One issue is scalability, which we address by developing\na stochastic variational inference method for Gaussian process (GP) preference\nlearning. We show how our method can be applied to predict argument\nconvincingness from crowdsourced data, outperforming the previous\nstate-of-the-art, particularly when trained with small amounts of unreliable\ndata. We demonstrate how the Bayesian approach enables more effective active\nlearning, thereby reducing the amount of data required to identify convincing\narguments for new users and domains. While word embeddings are principally used\nwith neural networks, our results show that word embeddings in combination with\nlinguistic features also benefit GPs when predicting argument convincingness.","url_abs":"http://arxiv.org/abs/1806.02418v1","url_pdf":"http://arxiv.org/pdf/1806.02418v1.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":"finding-convincing-arguments-using-scalable","repo_url":"https://github.com/UKPLab/tacl2018-preference-convincing","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"active-learning","task_name":"Active Learning"},{"task_slug":"variational-inference","task_name":"Variational Inference"},{"task_slug":"word-embeddings","task_name":"Word Embeddings"}],"methods":[{"method_slug":"gaussian-process","method_name":"Gaussian Process"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1806.02418","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}