Don't Parse, Choose Spans! Continuous and Discontinuous Constituency Parsing via Autoregressive Span Selection

Abstract

We present a simple and unified approach for both continuous and discontinuous constituency parsing via autoregressive span selection. Constituency parsing aims to produce a set of non-crossing spans so that they can form a constituency parse tree. We sort gold spans in a predefined order and train a pointer network to autoregressively select spans by that order. To deal with a discontinuous span, we consecutively select its subspans from left to right, label all but the last subspans with a special discontinuous label, and label the last subspan with the whole discontinuous span's label. We use a simple heuristic to output valid trees from selected spans so that our approach is able to predict all possible continuous and discontinuous constituency trees without sacrificing data coverage and without the need to use expensive chart-based parsing algorithms. Extensive experiments show that our model achieves state-of-the-art or competitive performance on all benchmarks of continuous and discontinuous constituency parsing.