{"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/are-pretrained-transformers-robust-in-intent","title":"Are Pretrained Transformers Robust in Intent Classification? A Missing Ingredient in Evaluation of Out-of-Scope Intent Detection","arxiv_id":"2106.04564","date":"2021-06-08","proceeding":null,"authors":["JianGuo Zhang","Kazuma Hashimoto","Yao Wan","Zhiwei Liu","Ye Liu","Caiming Xiong","Philip S. Yu"],"abstract":"Pre-trained Transformer-based models were reported to be robust in intent classification. In this work, we first point out the importance of in-domain out-of-scope detection in few-shot intent recognition tasks and then illustrate the vulnerability of pre-trained Transformer-based models against samples that are in-domain but out-of-scope (ID-OOS). We construct two new datasets, and empirically show that pre-trained models do not perform well on both ID-OOS examples and general out-of-scope examples, especially on fine-grained few-shot intent detection tasks. To figure out how the models mistakenly classify ID-OOS intents as in-scope intents, we further conduct analysis on confidence scores and the overlapping keywords, as well as point out several prospective directions for future work. Resources are available on https://github.com/jianguoz/Few-Shot-Intent-Detection.","url_abs":"https://arxiv.org/abs/2106.04564v3","url_pdf":"https://arxiv.org/pdf/2106.04564v3.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":"are-pretrained-transformers-robust-in-intent","repo_url":"https://github.com/jianguoz/Few-Shot-Intent-Detection","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}}],"tasks":[{"task_slug":"intent-classification","task_name":"Intent Classification"},{"task_slug":"intent-detection","task_name":"Intent Detection"},{"task_slug":"intent-recognition","task_name":"Intent Recognition"},{"task_slug":"intent-classification-1","task_name":"intent-classification"}],"methods":[],"datasets_introduced":[{"slug":"banking77-oos","name":"BANKING77-OOS","full_name":""},{"slug":"clinc-single-domain-oos","name":"CLINC-Single-Domain-OOS","full_name":""}],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2106.04564","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}