{"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/constructing-a-natural-language-inference","title":"Constructing a Natural Language Inference Dataset using Generative Neural Networks","arxiv_id":"1607.06025","date":"2016-07-20","proceeding":null,"authors":["Janez Starc","Dunja Mladenić"],"abstract":"Natural Language Inference is an important task for Natural Language\nUnderstanding. It is concerned with classifying the logical relation between\ntwo sentences. In this paper, we propose several text generative neural\nnetworks for generating text hypothesis, which allows construction of new\nNatural Language Inference datasets. To evaluate the models, we propose a new\nmetric -- the accuracy of the classifier trained on the generated dataset. The\naccuracy obtained by our best generative model is only 2.7% lower than the\naccuracy of the classifier trained on the original, human crafted dataset.\nFurthermore, the best generated dataset combined with the original dataset\nachieves the highest accuracy. The best model learns a mapping embedding for\neach training example. By comparing various metrics we show that datasets that\nobtain higher ROUGE or METEOR scores do not necessarily yield higher\nclassification accuracies. We also provide analysis of what are the\ncharacteristics of a good dataset including the distinguishability of the\ngenerated datasets from the original one.","url_abs":"http://arxiv.org/abs/1607.06025v2","url_pdf":"http://arxiv.org/pdf/1607.06025v2.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":"constructing-a-natural-language-inference","repo_url":"https://github.com/jstarc/nli_generation","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"natural-language-inference","task_name":"Natural Language Inference"},{"task_slug":"natural-language-understanding","task_name":"Natural Language Understanding"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}