@inproceedings{bugert-etal-2017-lsdsem,
title = "{LSDS}em 2017: Exploring Data Generation Methods for the Story Cloze Test",
author = {Bugert, Michael and
Puzikov, Yevgeniy and
R{\"u}ckl{\'e}, Andreas and
Eckle-Kohler, Judith and
Martin, Teresa and
Mart{\'\i}nez-C{\'a}mara, Eugenio and
Sorokin, Daniil and
Peyrard, Maxime and
Gurevych, Iryna},
editor = "Roth, Michael and
Mostafazadeh, Nasrin and
Chambers, Nathanael and
Louis, Annie",
booktitle = "Proceedings of the 2nd Workshop on Linking Models of Lexical, Sentential and Discourse-level Semantics",
month = apr,
year = "2017",
address = "Valencia, Spain",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W17-0908",
doi = "10.18653/v1/W17-0908",
pages = "56--61",
abstract = "The Story Cloze test is a recent effort in providing a common test scenario for text understanding systems. As part of the LSDSem 2017 shared task, we present a system based on a deep learning architecture combined with a rich set of manually-crafted linguistic features. The system outperforms all known baselines for the task, suggesting that the chosen approach is promising. We additionally present two methods for generating further training data based on stories from the ROCStories corpus.",
}
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<abstract>The Story Cloze test is a recent effort in providing a common test scenario for text understanding systems. As part of the LSDSem 2017 shared task, we present a system based on a deep learning architecture combined with a rich set of manually-crafted linguistic features. The system outperforms all known baselines for the task, suggesting that the chosen approach is promising. We additionally present two methods for generating further training data based on stories from the ROCStories corpus.</abstract>
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%0 Conference Proceedings
%T LSDSem 2017: Exploring Data Generation Methods for the Story Cloze Test
%A Bugert, Michael
%A Puzikov, Yevgeniy
%A Rücklé, Andreas
%A Eckle-Kohler, Judith
%A Martin, Teresa
%A Martínez-Cámara, Eugenio
%A Sorokin, Daniil
%A Peyrard, Maxime
%A Gurevych, Iryna
%Y Roth, Michael
%Y Mostafazadeh, Nasrin
%Y Chambers, Nathanael
%Y Louis, Annie
%S Proceedings of the 2nd Workshop on Linking Models of Lexical, Sentential and Discourse-level Semantics
%D 2017
%8 April
%I Association for Computational Linguistics
%C Valencia, Spain
%F bugert-etal-2017-lsdsem
%X The Story Cloze test is a recent effort in providing a common test scenario for text understanding systems. As part of the LSDSem 2017 shared task, we present a system based on a deep learning architecture combined with a rich set of manually-crafted linguistic features. The system outperforms all known baselines for the task, suggesting that the chosen approach is promising. We additionally present two methods for generating further training data based on stories from the ROCStories corpus.
%R 10.18653/v1/W17-0908
%U https://aclanthology.org/W17-0908
%U https://doi.org/10.18653/v1/W17-0908
%P 56-61
Markdown (Informal)
[LSDSem 2017: Exploring Data Generation Methods for the Story Cloze Test](https://aclanthology.org/W17-0908) (Bugert et al., LSDSem 2017)
ACL
- Michael Bugert, Yevgeniy Puzikov, Andreas Rücklé, Judith Eckle-Kohler, Teresa Martin, Eugenio Martínez-Cámara, Daniil Sorokin, Maxime Peyrard, and Iryna Gurevych. 2017. LSDSem 2017: Exploring Data Generation Methods for the Story Cloze Test. In Proceedings of the 2nd Workshop on Linking Models of Lexical, Sentential and Discourse-level Semantics, pages 56–61, Valencia, Spain. Association for Computational Linguistics.