2022 EMNLP EMNLP 2022

Reorder and then Parse, Fast and Accurate Discontinuous Constituency Parsing

Abstract

AbstractDiscontinuous constituency parsing is still kept developing for its efficiency and accuracy are far behind its continuous counterparts. Motivated by the observation that a discontinuous constituent tree can be simply transformed into a pseudo-continuous one by artificially reordering words in the sentence, we propose a novel reordering method, thereby construct fast and accurate discontinuous constituency parsing systems working in continuous way. Specifically, we model the relative position changes of words as a list of actions. By parsing and performing this actions, the corresponding pseudo-continuous sequence is derived. Discontinuous parse tree can be further inferred via integrating a high-performance pseudo-continuous constituency parser. Our systems are evaluated on three classical discontinuous constituency treebanks, achieving new state-of-the-art on two treebanks and showing a distinct advantage in speed.

🧭 Keyword Pioneer — pseudo-continuous sequence
🐝 Cross-Pollinator — Computer Science, Deep Learning, Natural Language Processing