We propose a novel transition-based algorithm that straightforwardly parses sentences from left to right by building @math attachments, with @math being the length of the input sentence. Similarly to the recent stack-pointer parser by (2018), we use the pointer network framework that, given a word, can directly point to a position from the sentence. However, our left-to-right approach is simpler than the original top-down stack-pointer parser (not requiring a stack) and reduces transition sequence length in half, from 2 @math -1 actions to @math . This results in a quadratic non-projective parser that runs twice as fast as the original while achieving the best accuracy to date on the English PTB dataset (96.04 UAS, 94.43 LAS) among fully-supervised single-model dependency parsers, and improves over the former top-down transition system in the majority of languages tested.
Almost all current dependency parsers classify based on millions of sparse indicator features. Not only do these features generalize poorly, but the cost of feature computation restricts parsing speed significantly. In this work, we propose a novel way of learning a neural network classifier for use in a greedy, transition-based dependency parser. Because this classifier learns and uses just a small number of dense features, it can work very fast, while achieving an about 2 improvement in unlabeled and labeled attachment scores on both English and Chinese datasets. Concretely, our parser is able to parse more than 1000 sentences per second at 92.2 unlabeled attachment score on the English Penn Treebank.