# End-to-end Graph-based TAG Parsing with Neural Networks

arXiv: https://arxiv.org/abs/1804.06610 · HTML: https://mlsanity.com/paper/1804.06610/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Apr 2018.

## Built on by (most cited first, 4 of 4)

- [Specializing Multilingual Language Models: An Empirical Study](https://mlsanity.com/paper/2106.09063/) (2021)
- [Something Old, Something New: Grammar-based CCG Parsing with Transformer Models](https://mlsanity.com/paper/2109.10044/) (2021)
- [jp-evalb: Robust Alignment-based PARSEVAL Measures](https://mlsanity.com/paper/2405.14150/) (2024)
- [Proposing TAGbank as a Corpus of Tree-Adjoining Grammar Derivations](https://mlsanity.com/paper/2504.05226/) (2025)

## Abstract (arXiv)

We present a graph-based Tree Adjoining Grammar (TAG) parser that uses BiLSTMs, highway connections, and character-level CNNs. Our best end-to-end parser, which jointly performs supertagging, POS tagging, and parsing, outperforms the previously reported best results by more than 2.2 LAS and UAS points. The graph-based parsing architecture allows for global inference and rich feature representations for TAG parsing, alleviating the fundamental trade-off between transition-based and graph-based parsing systems. We also demonstrate that the proposed parser achieves state-of-the-art performance in the downstream tasks of Parsing Evaluation using Textual Entailments (PETE) and Unbounded Dependency Recovery. This provides further support for the claim that TAG is a viable formalism for problems that require rich structural analysis of sentences.