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        <title>AMR Parsing with Causal Hierarchical Attention and Pointers</title>
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            <namePart type="given">Houda</namePart>
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    <abstract>Translation-based AMR parsers have recently gained popularity due to their simplicity and effectiveness. They predict linearized graphs as free texts, avoiding explicit structure modeling. However, this simplicity neglects structural locality in AMR graphs and introduces unnecessary tokens to represent coreferences. In this paper, we introduce new target forms of AMR parsing and a novel model, CHAP, which is equipped with causal hierarchical attention and the pointer mechanism, enabling the integration of structures into the Transformer decoder. We empirically explore various alternative modeling options. Experiments show that our model outperforms baseline models on four out of five benchmarks in the setting of no additional data.</abstract>
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    <identifier type="doi">10.18653/v1/2023.emnlp-main.553</identifier>
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%0 Conference Proceedings
%T AMR Parsing with Causal Hierarchical Attention and Pointers
%A Lou, Chao
%A Tu, Kewei
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F lou-tu-2023-amr
%X Translation-based AMR parsers have recently gained popularity due to their simplicity and effectiveness. They predict linearized graphs as free texts, avoiding explicit structure modeling. However, this simplicity neglects structural locality in AMR graphs and introduces unnecessary tokens to represent coreferences. In this paper, we introduce new target forms of AMR parsing and a novel model, CHAP, which is equipped with causal hierarchical attention and the pointer mechanism, enabling the integration of structures into the Transformer decoder. We empirically explore various alternative modeling options. Experiments show that our model outperforms baseline models on four out of five benchmarks in the setting of no additional data.
%R 10.18653/v1/2023.emnlp-main.553
%U https://aclanthology.org/2023.emnlp-main.553
%U https://doi.org/10.18653/v1/2023.emnlp-main.553
%P 8942-8955

Markdown (Informal)

[AMR Parsing with Causal Hierarchical Attention and Pointers](https://aclanthology.org/2023.emnlp-main.553) (Lou & Tu, EMNLP 2023)

• AMR Parsing with Causal Hierarchical Attention and Pointers (Lou & Tu, EMNLP 2023)

ACL

• Chao Lou and Kewei Tu. 2023. AMR Parsing with Causal Hierarchical Attention and Pointers. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 8942–8955, Singapore. Association for Computational Linguistics.