{"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/natural-language-inference-with-mixed-effects","title":"Natural Language Inference with Mixed Effects","arxiv_id":"2010.10501","date":"2020-10-20","proceeding":"Joint Conference on Lexical and Computational Semantics 2020","authors":["William Gantt","Benjamin Kane","Aaron Steven White"],"abstract":"There is growing evidence that the prevalence of disagreement in the raw annotations used to construct natural language inference datasets makes the common practice of aggregating those annotations to a single label problematic. We propose a generic method that allows one to skip the aggregation step and train on the raw annotations directly without subjecting the model to unwanted noise that can arise from annotator response biases. We demonstrate that this method, which generalizes the notion of a \\textit{mixed effects model} by incorporating \\textit{annotator random effects} into any existing neural model, improves performance over models that do not incorporate such effects.","url_abs":"https://arxiv.org/abs/2010.10501v1","url_pdf":"https://arxiv.org/pdf/2010.10501v1.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":"natural-language-inference-with-mixed-effects","repo_url":"https://github.com/wgantt/nli-mixed-models","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"natural-language-inference","task_name":"Natural Language Inference"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/2010.10501","atlas_url":"https://app.syntology.ai/?focus=2010.10501","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}