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

页码：4586 / 4592

页数：7

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