Tetra-Tagging: Word-Synchronous Parsing with Linear-Time Inference

Code

• github.com/nikitakit/tetra-tagging
  OfficialIn papernone★ 0
• github.com/yzhangcs/parser
  pytorch★ 878

Abstract

We present a constituency parsing algorithm that, like a supertagger, works by assigning labels to each word in a sentence. In order to maximally leverage current neural architectures, the model scores each word's tags in parallel, with minimal task-specific structure. After scoring, a left-to-right reconciliation phase extracts a tree in (empirically) linear time. Our parser achieves 95.4 F1 on the WSJ test set while also achieving substantial speedups compared to current state-of-the-art parsers with comparable accuracies.

Tasks

Benchmark Results

Reproductions