Meta Bi-LSTM’(Morphosyntactic Tagging with a Meta-BiLSTM Model over Context Sensitive Token Encodings)
Abstract - 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.
Paper - https://arxiv.org/pdf/1805.08237v1.pdf
Dataset - https://lindat.mff.cuni.cz/repository/xmlui/handle/11234/1-2184