Name
Abstract | ||
|---|---|---|
Neural attention models have achieved great success in different NLP tasks. How- ever, they have not fulfilled their promise on the AMR parsing task due to the data sparsity issue. In this paper, we de- scribe a sequence-to-sequence model for AMR parsing and present different ways to tackle the data sparsity problem. We show that our methods achieve significant improvement over a baseline neural atten- tion model and our results are also compet- itive against state-of-the-art systems that do not use extra linguistic resources. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.18653/v1/e17-1035 | conference of the european chapter of the association for computational linguistics |
Field | DocType | Volume |
Computer science,Natural language processing,Artificial intelligence,Parsing,Machine learning | Journal | abs/1702.05053 |
Citations | PageRank | References |
8 | 0.52 | 20 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xiaochang Peng | 1 | 54 | 5.31 |
| Chuan Wang | 2 | 8 | 0.52 |
| Daniel Gildea | 3 | 2269 | 193.43 |
| Nianwen Xue | 4 | 1654 | 117.65 |