{"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/noise-contrastive-estimation-and-negative","title":"Noise Contrastive Estimation and Negative Sampling for Conditional Models: Consistency and Statistical Efficiency","arxiv_id":"1809.01812","date":"2018-09-06","proceeding":"EMNLP 2018 10","authors":["Zhuang Ma","Michael Collins"],"abstract":"Noise Contrastive Estimation (NCE) is a powerful parameter estimation method\nfor log-linear models, which avoids calculation of the partition function or\nits derivatives at each training step, a computationally demanding step in many\ncases. It is closely related to negative sampling methods, now widely used in\nNLP. This paper considers NCE-based estimation of conditional models.\nConditional models are frequently encountered in practice; however there has\nnot been a rigorous theoretical analysis of NCE in this setting, and we will\nargue there are subtle but important questions when generalizing NCE to the\nconditional case. In particular, we analyze two variants of NCE for conditional\nmodels: one based on a classification objective, the other based on a ranking\nobjective. We show that the ranking-based variant of NCE gives consistent\nparameter estimates under weaker assumptions than the classification-based\nmethod; we analyze the statistical efficiency of the ranking-based and\nclassification-based variants of NCE; finally we describe experiments on\nsynthetic data and language modeling showing the effectiveness and trade-offs\nof both methods.","url_abs":"http://arxiv.org/abs/1809.01812v1","url_pdf":"http://arxiv.org/pdf/1809.01812v1.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":[],"tasks":[{"task_slug":"classification-1","task_name":"Classification"},{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"language-modeling","task_name":"Language Modeling"},{"task_slug":"language-modelling","task_name":"Language Modelling"},{"task_slug":"question-answering","task_name":"Question Answering"},{"task_slug":"parameter-estimation","task_name":"parameter estimation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/question-answering-on-wikiqa","task":"Question Answering","dataset":"WikiQA","model":"PairwiseRank +  Multi-Perspective CNN","rank_in_archive_order":14,"of":25,"metrics":{"MAP":"0.7010","MRR":"0.7180"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1809.01812","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}