@inproceedings{liu-2024-soft-well,
title = "Soft Well-Formed Semantic Parsing with Score-Based Selection",
author = "Liu, Jiangming",
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.1307",
pages = "15037--15043",
abstract = "Semantic parsing is the task of translating natural language into a structured, formal semantic representation that can be interpreted by machines. These semantic representations are organized with complex structures. While various models have been developed for semantic parsing, there has been limited focus on generating semantic representations with well-formed structures. In this study, we introduce a score-based method to select well-formed outputs from candidates generated by beam search algorithms. Our experiments focus on parsing texts into discourse representation structures, which are innovative semantic representations designed to capture the meaning of texts with arbitrary lengths across languages. Our experimental results demonstrate that models utilizing the proposed method can reduce the number of ill-formed outputs and improve F1 scores in English. Furthermore, our final model achieves significant improvements in German, Italian and Dutch zero-shot DRS parsing by effectively preventing ill-formed outputs.",
}
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<abstract>Semantic parsing is the task of translating natural language into a structured, formal semantic representation that can be interpreted by machines. These semantic representations are organized with complex structures. While various models have been developed for semantic parsing, there has been limited focus on generating semantic representations with well-formed structures. In this study, we introduce a score-based method to select well-formed outputs from candidates generated by beam search algorithms. Our experiments focus on parsing texts into discourse representation structures, which are innovative semantic representations designed to capture the meaning of texts with arbitrary lengths across languages. Our experimental results demonstrate that models utilizing the proposed method can reduce the number of ill-formed outputs and improve F1 scores in English. Furthermore, our final model achieves significant improvements in German, Italian and Dutch zero-shot DRS parsing by effectively preventing ill-formed outputs.</abstract>
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%0 Conference Proceedings
%T Soft Well-Formed Semantic Parsing with Score-Based Selection
%A Liu, Jiangming
%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 liu-2024-soft-well
%X Semantic parsing is the task of translating natural language into a structured, formal semantic representation that can be interpreted by machines. These semantic representations are organized with complex structures. While various models have been developed for semantic parsing, there has been limited focus on generating semantic representations with well-formed structures. In this study, we introduce a score-based method to select well-formed outputs from candidates generated by beam search algorithms. Our experiments focus on parsing texts into discourse representation structures, which are innovative semantic representations designed to capture the meaning of texts with arbitrary lengths across languages. Our experimental results demonstrate that models utilizing the proposed method can reduce the number of ill-formed outputs and improve F1 scores in English. Furthermore, our final model achieves significant improvements in German, Italian and Dutch zero-shot DRS parsing by effectively preventing ill-formed outputs.
%U https://aclanthology.org/2024.lrec-main.1307
%P 15037-15043
Markdown (Informal)
[Soft Well-Formed Semantic Parsing with Score-Based Selection](https://aclanthology.org/2024.lrec-main.1307) (Liu, LREC-COLING 2024)
ACL