@inproceedings{hamazono-etal-2020-market,
title = "Market Comment Generation from Data with Noisy Alignments",
author = "Hamazono, Yumi and
Uehara, Yui and
Noji, Hiroshi and
Miyao, Yusuke and
Takamura, Hiroya and
Kobayashi, Ichiro",
editor = "Davis, Brian and
Graham, Yvette and
Kelleher, John and
Sripada, Yaji",
booktitle = "Proceedings of the 13th International Conference on Natural Language Generation",
month = dec,
year = "2020",
address = "Dublin, Ireland",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.inlg-1.21",
doi = "10.18653/v1/2020.inlg-1.21",
pages = "148--157",
abstract = "End-to-end models on data-to-text learn the mapping of data and text from the aligned pairs in the dataset. However, these alignments are not always obtained reliably, especially for the time-series data, for which real time comments are given to some situation and there might be a delay in the comment delivery time compared to the actual event time. To handle this issue of possible noisy alignments in the dataset, we propose a neural network model with multi-timestep data and a copy mechanism, which allows the models to learn the correspondences between data and text from the dataset with noisier alignments. We focus on generating market comments in Japanese that are delivered each time an event occurs in the market. The core idea of our approach is to utilize multi-timestep data, which is not only the latest market price data when the comment is delivered, but also the data obtained at several timesteps earlier. On top of this, we employ a copy mechanism that is suitable for referring to the content of data records in the market price data. We confirm the superiority of our proposal by two evaluation metrics and show the accuracy improvement of the sentence generation using the time series data by our proposed method.",
}
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<abstract>End-to-end models on data-to-text learn the mapping of data and text from the aligned pairs in the dataset. However, these alignments are not always obtained reliably, especially for the time-series data, for which real time comments are given to some situation and there might be a delay in the comment delivery time compared to the actual event time. To handle this issue of possible noisy alignments in the dataset, we propose a neural network model with multi-timestep data and a copy mechanism, which allows the models to learn the correspondences between data and text from the dataset with noisier alignments. We focus on generating market comments in Japanese that are delivered each time an event occurs in the market. The core idea of our approach is to utilize multi-timestep data, which is not only the latest market price data when the comment is delivered, but also the data obtained at several timesteps earlier. On top of this, we employ a copy mechanism that is suitable for referring to the content of data records in the market price data. We confirm the superiority of our proposal by two evaluation metrics and show the accuracy improvement of the sentence generation using the time series data by our proposed method.</abstract>
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%0 Conference Proceedings
%T Market Comment Generation from Data with Noisy Alignments
%A Hamazono, Yumi
%A Uehara, Yui
%A Noji, Hiroshi
%A Miyao, Yusuke
%A Takamura, Hiroya
%A Kobayashi, Ichiro
%Y Davis, Brian
%Y Graham, Yvette
%Y Kelleher, John
%Y Sripada, Yaji
%S Proceedings of the 13th International Conference on Natural Language Generation
%D 2020
%8 December
%I Association for Computational Linguistics
%C Dublin, Ireland
%F hamazono-etal-2020-market
%X End-to-end models on data-to-text learn the mapping of data and text from the aligned pairs in the dataset. However, these alignments are not always obtained reliably, especially for the time-series data, for which real time comments are given to some situation and there might be a delay in the comment delivery time compared to the actual event time. To handle this issue of possible noisy alignments in the dataset, we propose a neural network model with multi-timestep data and a copy mechanism, which allows the models to learn the correspondences between data and text from the dataset with noisier alignments. We focus on generating market comments in Japanese that are delivered each time an event occurs in the market. The core idea of our approach is to utilize multi-timestep data, which is not only the latest market price data when the comment is delivered, but also the data obtained at several timesteps earlier. On top of this, we employ a copy mechanism that is suitable for referring to the content of data records in the market price data. We confirm the superiority of our proposal by two evaluation metrics and show the accuracy improvement of the sentence generation using the time series data by our proposed method.
%R 10.18653/v1/2020.inlg-1.21
%U https://aclanthology.org/2020.inlg-1.21
%U https://doi.org/10.18653/v1/2020.inlg-1.21
%P 148-157
Markdown (Informal)
[Market Comment Generation from Data with Noisy Alignments](https://aclanthology.org/2020.inlg-1.21) (Hamazono et al., INLG 2020)
ACL
- Yumi Hamazono, Yui Uehara, Hiroshi Noji, Yusuke Miyao, Hiroya Takamura, and Ichiro Kobayashi. 2020. Market Comment Generation from Data with Noisy Alignments. In Proceedings of the 13th International Conference on Natural Language Generation, pages 148–157, Dublin, Ireland. Association for Computational Linguistics.