Simple and Accurate Dependency Parsing Using Bidirectional LSTM Feature Representations

Understand

We present a simple and effective scheme for dependency parsing which is based on bidirectional-LSTMs (BiLSTMs).

• Each sentence token is associated with a BiLSTM vector representing the token in its sentential context, and feature vectors are constructed by concatenating a few BiLSTM vectors.
• The BiLSTM is trained jointly with the parser objective, resulting in very effective feature extractors for parsing.
• We demonstrate the effectiveness of the approach by applying it to a greedy transition-based parser as well as to a globally optimized graph-based parser.