Papers › Tetra-Tagging: Word-Synchronous Parsing with Linear-Time Inference
Tetra-Tagging: Word-Synchronous Parsing with Linear-Time Inference
Nikita Kitaev, Dan Klein
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.
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Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Constituency Parsing | Penn Treebank | Tetra Tagging | F1 score | 95.44 | #14 of 27 | Archive leaderboard | report |
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