Paper: Importance weighting and unsupervised domain adaptation of POS taggers: a negative result

ACL ID D14-1104
Title Importance weighting and unsupervised domain adaptation of POS taggers: a negative result
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2014
Authors

Importance weighting is a generalization of various statistical bias correction tech- niques. While our labeled data in NLP is heavily biased, importance weighting has seen only few applications in NLP, most of them relying on a small amount of labeled target data. The publication bias toward reporting positive results makes it hard to say whether researchers have tried. This paper presents a negative result on unsu- pervised domain adaptation for POS tag- ging. In this setup, we only have unlabeled data and thus only indirect access to the bias in emission and transition probabili- ties. Moreover, most errors in POS tag- ging are due to unseen words, and there, importance weighting cannot help. We present experiments with a wide variety of weight functions, quantilizations, as well as wit...