Simple and Accurate Dependency Parsing Using Bidirectional LSTM Feature Representations
This work provides a simple and effective method for dependency parsing, benefiting NLP researchers and practitioners, though it is incremental as it builds on existing LSTM and parser frameworks.
The authors tackled dependency parsing by using bidirectional LSTM feature representations trained jointly with parser objectives, achieving state-of-the-art accuracies on English and Chinese datasets.
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. The resulting parsers have very simple architectures, and match or surpass the state-of-the-art accuracies on English and Chinese.