%0 Conference Proceedings %T Morphosyntactic Tagging with a Meta-BiLSTM Model over Context Sensitive Token Encodings %A Bohnet, Bernd %A McDonald, Ryan %A Simões, Gonçalo %A Andor, Daniel %A Pitler, Emily %A Maynez, Joshua %Y Gurevych, Iryna %Y Miyao, Yusuke %S Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) %D 2018 %8 July %I Association for Computational Linguistics %C Melbourne, Australia %F bohnet-etal-2018-morphosyntactic %X The rise of neural networks, and particularly recurrent neural networks, has produced significant advances in part-of-speech tagging accuracy. One characteristic common among these models is the presence of rich initial word encodings. These encodings typically are composed of a recurrent character-based representation with dynamically and pre-trained word embeddings. However, these encodings do not consider a context wider than a single word and it is only through subsequent recurrent layers that word or sub-word information interacts. In this paper, we investigate models that use recurrent neural networks with sentence-level context for initial character and word-based representations. In particular we show that optimal results are obtained by integrating these context sensitive representations through synchronized training with a meta-model that learns to combine their states. %R 10.18653/v1/P18-1246 %U https://aclanthology.org/P18-1246 %U https://doi.org/10.18653/v1/P18-1246 %P 2642-2652