TestAug: A Framework for Augmenting Capability-based NLP Tests

1 code implementation • COLING 2022 • Guanqun Yang, Mirazul Haque, Qiaochu Song, Wei Yang, Xueqing Liu

Our experiments show that TestAug has three advantages over the existing work on behavioral testing: (1) TestAug can find more bugs than existing work; (2) The test cases in TestAug are more diverse; and (3) TestAug largely saves the manual efforts in creating the test suites.

Few-Sample Named Entity Recognition for Security Vulnerability Reports by Fine-Tuning Pre-Trained Language Models

1 code implementation • 14 Aug 2021 • Guanqun Yang, Shay Dineen, Zhipeng Lin, Xueqing Liu

In this work, for the first time, we propose to investigate this problem where only a small number of labeled training samples are available.

named-entity-recognition NER +1