@inproceedings{murakami-etal-2021-generating,
title = "Generating Weather Comments from Meteorological Simulations",
author = "Murakami, Soichiro and
Tanaka, Sora and
Hangyo, Masatsugu and
Kamigaito, Hidetaka and
Funakoshi, Kotaro and
Takamura, Hiroya and
Okumura, Manabu",
editor = "Merlo, Paola and
Tiedemann, Jorg and
Tsarfaty, Reut",
booktitle = "Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume",
month = apr,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.eacl-main.125/",
doi = "10.18653/v1/2021.eacl-main.125",
pages = "1462--1473",
abstract = "The task of generating weather-forecast comments from meteorological simulations has the following requirements: (i) the changes in numerical values for various physical quantities need to be considered, (ii) the weather comments should be dependent on delivery time and area information, and (iii) the comments should provide useful information for users. To meet these requirements, we propose a data-to-text model that incorporates three types of encoders for numerical forecast maps, observation data, and meta-data. We also introduce weather labels representing weather information, such as sunny and rain, for our model to explicitly describe useful information. We conducted automatic and human evaluations. The results indicate that our model performed best against baselines in terms of informativeness. We make our code and data publicly available."
}
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<abstract>The task of generating weather-forecast comments from meteorological simulations has the following requirements: (i) the changes in numerical values for various physical quantities need to be considered, (ii) the weather comments should be dependent on delivery time and area information, and (iii) the comments should provide useful information for users. To meet these requirements, we propose a data-to-text model that incorporates three types of encoders for numerical forecast maps, observation data, and meta-data. We also introduce weather labels representing weather information, such as sunny and rain, for our model to explicitly describe useful information. We conducted automatic and human evaluations. The results indicate that our model performed best against baselines in terms of informativeness. We make our code and data publicly available.</abstract>
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%0 Conference Proceedings
%T Generating Weather Comments from Meteorological Simulations
%A Murakami, Soichiro
%A Tanaka, Sora
%A Hangyo, Masatsugu
%A Kamigaito, Hidetaka
%A Funakoshi, Kotaro
%A Takamura, Hiroya
%A Okumura, Manabu
%Y Merlo, Paola
%Y Tiedemann, Jorg
%Y Tsarfaty, Reut
%S Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume
%D 2021
%8 April
%I Association for Computational Linguistics
%C Online
%F murakami-etal-2021-generating
%X The task of generating weather-forecast comments from meteorological simulations has the following requirements: (i) the changes in numerical values for various physical quantities need to be considered, (ii) the weather comments should be dependent on delivery time and area information, and (iii) the comments should provide useful information for users. To meet these requirements, we propose a data-to-text model that incorporates three types of encoders for numerical forecast maps, observation data, and meta-data. We also introduce weather labels representing weather information, such as sunny and rain, for our model to explicitly describe useful information. We conducted automatic and human evaluations. The results indicate that our model performed best against baselines in terms of informativeness. We make our code and data publicly available.
%R 10.18653/v1/2021.eacl-main.125
%U https://aclanthology.org/2021.eacl-main.125/
%U https://doi.org/10.18653/v1/2021.eacl-main.125
%P 1462-1473
Markdown (Informal)
[Generating Weather Comments from Meteorological Simulations](https://aclanthology.org/2021.eacl-main.125/) (Murakami et al., EACL 2021)
ACL
- Soichiro Murakami, Sora Tanaka, Masatsugu Hangyo, Hidetaka Kamigaito, Kotaro Funakoshi, Hiroya Takamura, and Manabu Okumura. 2021. Generating Weather Comments from Meteorological Simulations. In Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pages 1462–1473, Online. Association for Computational Linguistics.