{"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/several-experiments-on-investigating","title":"Several Experiments on Investigating Pretraining and Knowledge-Enhanced Models for Natural Language Inference","arxiv_id":"1904.12104","date":"2019-04-27","proceeding":null,"authors":["Tianda Li","Xiaodan Zhu","Quan Liu","Qian Chen","Zhigang Chen","Si Wei"],"abstract":"Natural language inference (NLI) is among the most challenging tasks in\nnatural language understanding. Recent work on unsupervised pretraining that\nleverages unsupervised signals such as language-model and sentence prediction\nobjectives has shown to be very effective on a wide range of NLP problems. It\nwould still be desirable to further understand how it helps NLI; e.g., if it\nlearns artifacts in data annotation or instead learn true inference knowledge.\nIn addition, external knowledge that does not exist in the limited amount of\nNLI training data may be added to NLI models in two typical ways, e.g., from\nhuman-created resources or an unsupervised pretraining paradigm. We runs\nseveral experiments here to investigate whether they help NLI in the same way,\nand if not,how?","url_abs":"http://arxiv.org/abs/1904.12104v1","url_pdf":"http://arxiv.org/pdf/1904.12104v1.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":"several-experiments-on-investigating","repo_url":"https://github.com/TeddLi/Further-manually-annotated-glockner","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"language-modeling","task_name":"Language Modeling"},{"task_slug":"language-modelling","task_name":"Language Modelling"},{"task_slug":"natural-language-inference","task_name":"Natural Language Inference"},{"task_slug":"natural-language-understanding","task_name":"Natural Language Understanding"},{"task_slug":"sentence","task_name":"Sentence"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1904.12104","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}