Are Pretrained Transformers Robust in Intent Classification? A Missing Ingredient in Evaluation of Out-of-Scope Intent Detection

• URL: http://arxiv.org/abs/2106.04564v1
• Date: Tue, 8 Jun 2021 17:51:12 GMT
• Title: Are Pretrained Transformers Robust in Intent Classification? A Missing Ingredient in Evaluation of Out-of-Scope Intent Detection
• Authors: Jian-Guo Zhang, Kazuma Hashimoto, Yao Wan, Ye Liu, Caiming Xiong, Philip S. Yu
• Abstract summary: We first point out the importance of in-domain out-of-scope detection in few-shot intent recognition tasks. We then illustrate the vulnerability of pretrained Transformer-based models against samples that are in-domain but out-of-scope (ID-OOS)
• Score: 93.40525251094071
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Pretrained 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 pretrained Transformer-based models against samples that are in-domain but out-of-scope (ID-OOS). We empirically show that pretrained 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 and provide several prospective directions for future work. We release the relevant resources to facilitate future research.

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