Name
Abstract | ||
|---|---|---|
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. |
Year | Venue | DocType |
|---|---|---|
2018 | NAACL-HLT | Journal |
Volume | Citations | PageRank |
abs/1804.06610 | 0 | 0.34 |
References | Authors | |
19 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jungo Kasai | 1 | 7 | 3.85 |
| Robert Frank | 2 | 7 | 4.59 |
| Pauli Xu | 3 | 0 | 0.34 |
| William Merrill | 4 | 1 | 2.04 |
| Owen Rambow | 5 | 2256 | 247.69 |