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        <title>Rewarding Smatch: Transition-Based AMR Parsing with Reinforcement Learning</title>
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        <namePart type="given">Tahira</namePart>
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    <name type="personal">
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    <abstract>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.</abstract>
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%0 Conference Proceedings
%T Rewarding Smatch: Transition-Based AMR Parsing with Reinforcement Learning
%A Naseem, Tahira
%A Shah, Abhishek
%A Wan, Hui
%A Florian, Radu
%A Roukos, Salim
%A Ballesteros, Miguel
%Y Korhonen, Anna
%Y Traum, David
%Y Màrquez, Lluís
%S Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
%D 2019
%8 July
%I Association for Computational Linguistics
%C Florence, Italy
%F naseem-etal-2019-rewarding
%X 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.
%R 10.18653/v1/P19-1451
%U https://aclanthology.org/P19-1451
%U https://doi.org/10.18653/v1/P19-1451
%P 4586-4592

Markdown (Informal)

[Rewarding Smatch: Transition-Based AMR Parsing with Reinforcement Learning](https://aclanthology.org/P19-1451) (Naseem et al., ACL 2019)

• Rewarding Smatch: Transition-Based AMR Parsing with Reinforcement Learning (Naseem et al., ACL 2019)

ACL

• Tahira Naseem, Abhishek Shah, Hui Wan, Radu Florian, Salim Roukos, and Miguel Ballesteros. 2019. Rewarding Smatch: Transition-Based AMR Parsing with Reinforcement Learning. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 4586–4592, Florence, Italy. Association for Computational Linguistics.