Paper Metadata{2021.findings-emnlp.96}, closes #3887.

data/xml/2021.findings.xml

@@ -7954,8 +7954,8 @@
       <author><first>Li-Ming</first><last>Zhan</last></author>
       <author><first>Jiaxin</first><last>Chen</last></author>
       <author><first>Guangyuan</first><last>Shi</last></author>
-      <author><first>Xiao-Ming</first><last>Wu</last></author>
       <author><first>Albert Y.S.</first><last>Lam</last></author>
+      <author><first>Xiao-Ming</first><last>Wu</last></author>
       <pages>1114–1120</pages>
       <abstract>This paper investigates the effectiveness of pre-training for few-shot intent classification. While existing paradigms commonly further pre-train language models such as BERT on a vast amount of unlabeled corpus, we find it highly effective and efficient to simply fine-tune BERT with a small set of labeled utterances from public datasets. Specifically, fine-tuning BERT with roughly 1,000 labeled data yields a pre-trained model – IntentBERT, which can easily surpass the performance of existing pre-trained models for few-shot intent classification on novel domains with very different semantics. The high effectiveness of IntentBERT confirms the feasibility and practicality of few-shot intent detection, and its high generalization ability across different domains suggests that intent classification tasks may share a similar underlying structure, which can be efficiently learned from a small set of labeled data. The source code can be found at <url>https://github.com/hdzhang-code/IntentBERT</url>.</abstract>
       <url hash="297df895">2021.findings-emnlp.96</url>