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An increasingly wide range of artificial intelligence applications rely onsyntactic information to process and extract meaning from natural language textor speech, with constituent trees being one of the most widely used syntacticformalisms. To produce these phrase-structure representations from sentences innatural language, shift-reduce constituent parsers have become one of the mostefficient approaches. Increasing their accuracy and speed is still one of themain objectives pursued by the research community so that artificialintelligence applications that make use of parsing outputs, such as machinetranslation or voice assistant services, can improve their performance. Withthis goal in mind, we propose in this article a novel non-binary shift-reducealgorithm for constituent parsing. Our parser follows a classical bottom-upstrategy but, unlike others, it straightforwardly creates non-binary branchingswith just one Reduce transition, instead of requiring prior binarization or asequence of binary transitions, allowing its direct application to any languagewithout the need of further resources such as percolation tables. As a result,it uses fewer transitions per sentence than existing transition-basedconstituent parsers, becoming the fastest such system and, as a consequence,speeding up downstream applications. Using static oracle training and greedysearch, the accuracy of this novel approach is on par with state-of-the-arttransition-based constituent parsers and outperforms all top-down and bottom-upgreedy shift-reduce systems on the Wall Street Journal section from the EnglishPenn Treebank and the Penn Chinese Treebank. Additionally, we develop a dynamicoracle for training the proposed transition-based algorithm, achieving furtherimprovements in both benchmarks and obtaining the best accuracy to date on thePenn Chinese Treebank among greedy shift-reduce parsers.

Faster Shift-Reduce Constituent Parsing with a Non-Binary, Bottom-Up Strategy