Inducing Multilingual Text Analysis Tools Using Bidirectional Recurrent Neural Networks
Table 1: Token-level POS tagging accuracy for Simple Projection, SRNN using MultiVec bilingual word embeddings as input, RNN444For RNN models, only one (same) system is used to tag German, Greek and Spanish, Projection+RNN and methods of Das & Petrov (2011), Duong et al (2013) and Gouws & Søgaard (2015).
['[25mm] [BOLD] ModelLang.', '[BOLD] French All words', '[BOLD] French OOV', '[BOLD] German All words', '[BOLD] German OOV', '[BOLD] Greek All words', '[BOLD] Greek OOV', '[BOLD] Spanish All words', '[BOLD] Spanish OOV']
We note that the POS tagger based on bidirectional RNN (BRNN) has better performance than simple RNN (SRNN), which means that both past and future contexts help select the correct tag. It is shown that after replacing OOVs by the closest words using CBOW, the tagging accuracy significantly increases.