Faster Shift-Reduce Constituent Parsing with a Non-Binary, Bottom-Up Strategy 1footnote 11footnote 1© 2019. This is the final peer-reviewed manuscript that was accepted for publication at Artificial Intelligence and made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/. This may not reflect subsequent changes resulting from the publishing process such as editing, formatting, pagination, and other quality control mechanisms. The final journal publication is available at https://doi.org/10.1016/j.artint.2019.07.006.
Table 6: Accuracy and speed comparison of our approach against current state-of-the-art chart-based constituent parsers on WSJ §23.
['Parser', 'F1', 'sent./s.']
In order to put our approach into context, we provide a comparison against four of the current state-of-the-art systems in performing constituent parsing. In particular, these parsers make use of global chart decoding to achieve the highest accuracies to date on WSJ in cubic running time, with a large cost in speed in comparison to shift-reduce systems. This difference in speed, as well as the capability of incrementally processing the input from left to right, makes bottom-up shift-reduce parsers appealing for some downstream NLP applications, such as real-time machine translation systems, that require to produce a practically immediate output while the input is still coming.