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

TL;DR

This paper introduces a novel active learning strategy for POS tagging that focuses on reducing confusion between tag pairs, outperforming existing heuristics across multiple languages.

Contribution

The paper proposes a new active learning approach targeting confusion reduction between tag pairs, improving sample selection for low-resource POS tagging.

Findings

Proposed strategy outperforms existing AL heuristics significantly.

Proper model calibration via cross-view training enhances AL effectiveness.

Selected examples better match oracle data distribution.

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.