Ten Pairs to Tag – Multilingual POS Tagging via Coarse Mapping between Embeddings

摘要

In the absence of annotations in the target language, multilingual models typically draw on extensive parallel resources. In this paper, we demonstrate that accurate multilingual part-of-speech (POS) tagging can be done with just a few (e.g., ten) word translation pairs. We use the translation pairs to establish a coarse linear isometric (orthonormal) mapping be-tween monolingual embeddings. This enables the supervised source model expressed in terms of embeddings to be used directly on the target language. We further refine the model in an unsupervised manner by initializing and regularizing it to be close to the direct transfer model. Averaged across six languages, our model yields a 37.5% absolute improvement over the monolingual prototype-driven method (Haghighi and Klein, 2006) when using a comparable amount of super-vision. Moreover, to highlight key linguistic characteristics of the generated tags, we use them to predict typological properties of languages, obtaining a 50% error reduction relative to the prototype model. 1