Reducing Confusion in Active Learning for Part-Of-Speech Tagging

• Main Authors: NAACL 2021 2021, Anastasopoulos, Antonios, Chaudhary, Aditi, Neubig, Graham, Sheikh, Zaid
• Format: Article in Journal/Newspaper
• Language: unknown
• Published: Underline Science Inc. 2021
• Subjects: Artificial Intelligence; Computer Science and Engineering; Intelligent System; Natural Language Processing; sami
• Online Access: https://dx.doi.org/10.48448/yd5y-fv44; https://underline.io/lecture/20053-reducing-confusion-in-active-learning-for-part-of-speech-tagging

Read the paper on the folowing link: https://transacl.org/ojs/index.php/tacl/article/view/2155 Abstract: Active learning (AL) uses a data selection algorithm to select useful training samples to minimize annotation cost. This is now an essential tool for building low-resource syntactic analyzers such as part-of-speech (POS) taggers. Existing AL heuristics are generally designed on the principle of selecting uncertain yet representative training instances, where annotating these instances may reduce a large number of errors. However, in an empirical study across six typologically diverse languages (German, Swedish, Galician, North Sami, Persian, and Ukrainian), we found the surprising result that even in an oracle scenario where we know the true uncertainty of predictions, these current heuristics are far from optimal. Based on this analysis, we pose the problem of AL as selecting instances which maximally reduce the confusion between particular pairs of output tags. Extensive experimentation on the aforementioned languages shows that our proposed AL strategy outperforms other AL strategies by a significant margin. We also present auxiliary results demonstrating the importance of proper calibration of models, which we ensure through cross-view training, and analysis demonstrating how our proposed strategy selects examples that more closely follow the oracle data distribution.