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 learned 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. We
present results on part-of-speech and morphological tagging with
state-of-the-art performance on a number of languages.

本研究探讨了将具有句子级上下文信息的递归神经网络用于初始化字符和单词级别的表示，并通过元模型进行同步训练来优化其性能，以实现词性标注和形态标记的最佳性能表现。