Name
Abstract | ||
|---|---|---|
Syntactic constituency parsing is a fundamental problem in natural language processing and has been the subject of intensive research and engineering for decades. As a result, the most accurate parsers are domain specific, complex, and inefficient. In this paper we show that the domain agnostic attention-enhanced sequence-to-sequence model achieves state-of-the-art results on the most widely used syntactic constituency parsing dataset, when trained on a large synthetic corpus that was annotated using existing parsers. It also matches the performance of standard parsers when trained only on a small human-annotated dataset, which shows that this model is highly data-efficient, in contrast to sequence-to-sequence models without the attention mechanism. Our parser is also fast, processing over a hundred sentences per second with an unoptimized CPU implementation. |
Year | Venue | Field |
|---|---|---|
2014 | neural information processing systems | Top-down parsing language,Top-down parsing,LR parser,Computer science,Speech recognition,Grammar,Parsing expression grammar,Natural language processing,Artificial intelligence,Parser combinator,Parsing,Syntax |
DocType | Volume | ISSN |
Journal | abs/1412.7449 | 1049-5258 |
Citations | PageRank | References |
237 | 10.73 | 27 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Oriol Vinyals | 1 | 9419 | 418.45 |
| Łukasz Kaiser | 2 | 2307 | 89.08 |
| Koo, Terry | 3 | 237 | 10.73 |
| Slav Petrov | 4 | 2405 | 107.56 |
| Ilya Sutskever | 5 | 25814 | 1120.24 |
| geoffrey e hinton | 6 | 40435 | 4751.69 |