Rewarding Smatch: Transition-Based AMR Parsing with Reinforcement Learning

被引：0
|
作者：
Naseem, Tahira [1 ]
Shah, Abhishek [2 ]
Wan, Hui [1 ]
Florian, Radu [1 ]
Roukos, Salim [1 ]
Ballesteros, Miguel [1 ]
机构：
[1] IBM Res, Yorktown Hts, NY 10598 USA
[2] IBM Watson, New York, NY USA
来源：
57TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2019) | 2019年
关键词：
ALGORITHMS;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Our work involves enriching the Stack-LSTM transition-based AMR parser (Ballesteros and Al-Onaizan, 2017) by augmenting training with Policy Learning and rewarding the Smatch score of sampled graphs. In addition, we also combined several AMR-to-text alignments with an attention mechanism and we supplemented the parser with pre-processed concept identification, named entities and contextualized embeddings. We achieve a highly competitive performance that is comparable to the best published results. We show an in-depth study ablating each of the new components of the parser.
引用
收藏
页码：4586 / 4592
页数：7
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