@inproceedings{fan-etal-2024-leveraging,
title = "Leveraging {AMR} Graph Structure for Better Sequence-to-Sequence {AMR} Parsing",
author = "Fan, Linyu and
Yiheng, Wu Wu and
Xie, Jun and
Li, Junhui and
Kong, Fang and
Zhou, Guodong",
editor = "Calzolari, Nicoletta and
Kan, Min-Yen and
Hoste, Veronique and
Lenci, Alessandro and
Sakti, Sakriani and
Xue, Nianwen",
booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
month = may,
year = "2024",
address = "Torino, Italia",
publisher = "ELRA and ICCL",
url = "https://aclanthology.org/2024.lrec-main.903/",
pages = "10336--10346",
abstract = "Thanks to the development of pre-trained sequence-to-sequence (seq2seq) models (e.g., BART), recent studies on AMR parsing often regard this task as a seq2seq translation problem by linearizing AMR graphs into AMR token sequences in pre-processing and recovering AMR graphs from sequences in post-processing. Seq2seq AMR parsing is a relatively simple paradigm but it unavoidably loses structural information among AMR tokens. To compensate for the loss of structural information, in this paper we explicitly leverage AMR structure in the decoding phase. Given an AMR graph, we first project the structure in the graph into an AMR token graph, i.e., structure among AMR tokens in the linearized sequence. The structures for an AMR token could be divided into two parts: structure in prediction history and structure in future. Then we propose to model structure in prediction history via a graph attention network (GAT) and learn structure in future via a multi-task scheme, respectively. Experimental results show that our approach significantly outperforms a strong baseline and achieves performance with 85.5 {\ensuremath{\pm}}0.1 and 84.2 {\ensuremath{\pm}}0.1 Smatch scores on AMR 2.0 and AMR 3.0, respectively"
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<abstract>Thanks to the development of pre-trained sequence-to-sequence (seq2seq) models (e.g., BART), recent studies on AMR parsing often regard this task as a seq2seq translation problem by linearizing AMR graphs into AMR token sequences in pre-processing and recovering AMR graphs from sequences in post-processing. Seq2seq AMR parsing is a relatively simple paradigm but it unavoidably loses structural information among AMR tokens. To compensate for the loss of structural information, in this paper we explicitly leverage AMR structure in the decoding phase. Given an AMR graph, we first project the structure in the graph into an AMR token graph, i.e., structure among AMR tokens in the linearized sequence. The structures for an AMR token could be divided into two parts: structure in prediction history and structure in future. Then we propose to model structure in prediction history via a graph attention network (GAT) and learn structure in future via a multi-task scheme, respectively. Experimental results show that our approach significantly outperforms a strong baseline and achieves performance with 85.5 \ensuremath\pm0.1 and 84.2 \ensuremath\pm0.1 Smatch scores on AMR 2.0 and AMR 3.0, respectively</abstract>
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%0 Conference Proceedings
%T Leveraging AMR Graph Structure for Better Sequence-to-Sequence AMR Parsing
%A Fan, Linyu
%A Yiheng, Wu Wu
%A Xie, Jun
%A Li, Junhui
%A Kong, Fang
%A Zhou, Guodong
%Y Calzolari, Nicoletta
%Y Kan, Min-Yen
%Y Hoste, Veronique
%Y Lenci, Alessandro
%Y Sakti, Sakriani
%Y Xue, Nianwen
%S Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)
%D 2024
%8 May
%I ELRA and ICCL
%C Torino, Italia
%F fan-etal-2024-leveraging
%X Thanks to the development of pre-trained sequence-to-sequence (seq2seq) models (e.g., BART), recent studies on AMR parsing often regard this task as a seq2seq translation problem by linearizing AMR graphs into AMR token sequences in pre-processing and recovering AMR graphs from sequences in post-processing. Seq2seq AMR parsing is a relatively simple paradigm but it unavoidably loses structural information among AMR tokens. To compensate for the loss of structural information, in this paper we explicitly leverage AMR structure in the decoding phase. Given an AMR graph, we first project the structure in the graph into an AMR token graph, i.e., structure among AMR tokens in the linearized sequence. The structures for an AMR token could be divided into two parts: structure in prediction history and structure in future. Then we propose to model structure in prediction history via a graph attention network (GAT) and learn structure in future via a multi-task scheme, respectively. Experimental results show that our approach significantly outperforms a strong baseline and achieves performance with 85.5 \ensuremath\pm0.1 and 84.2 \ensuremath\pm0.1 Smatch scores on AMR 2.0 and AMR 3.0, respectively
%U https://aclanthology.org/2024.lrec-main.903/
%P 10336-10346
Markdown (Informal)
[Leveraging AMR Graph Structure for Better Sequence-to-Sequence AMR Parsing](https://aclanthology.org/2024.lrec-main.903/) (Fan et al., LREC-COLING 2024)
ACL