mlsanityData to 7 Oct 2026

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

arXiv 1904.09745 ↗First posted 22 Apr 2019cs.CLcited by 17

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Abstract (arXiv)

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.

Key facts. Cited by 17 papers in the map (data through 7 Oct 2026). First posted on arXiv 22 Apr 2019.

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Citing papers per year: 2021: 2; 2022: 3; 2023: 6; 2024: 2; 2025: 4.

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