@inproceedings{wang-etal-2018-picking,
title = "Picking Apart Story Salads",
author = "Wang, Su and
Holgate, Eric and
Durrett, Greg and
Erk, Katrin",
editor = "Riloff, Ellen and
Chiang, David and
Hockenmaier, Julia and
Tsujii, Jun{'}ichi",
booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
month = oct # "-" # nov,
year = "2018",
address = "Brussels, Belgium",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D18-1175",
doi = "10.18653/v1/D18-1175",
pages = "1455--1465",
abstract = "During natural disasters and conflicts, information about what happened is often confusing and messy, and distributed across many sources. We would like to be able to automatically identify relevant information and assemble it into coherent narratives of what happened. To make this task accessible to neural models, we introduce \textit{Story Salads}, mixtures of multiple documents that can be generated at scale. By exploiting the Wikipedia hierarchy, we can generate salads that exhibit challenging inference problems. Story salads give rise to a novel, challenging clustering task, where the objective is to group sentences from the same narratives. We demonstrate that simple bag-of-words similarity clustering falls short on this task, and that it is necessary to take into account global context and coherence.",
}
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<abstract>During natural disasters and conflicts, information about what happened is often confusing and messy, and distributed across many sources. We would like to be able to automatically identify relevant information and assemble it into coherent narratives of what happened. To make this task accessible to neural models, we introduce Story Salads, mixtures of multiple documents that can be generated at scale. By exploiting the Wikipedia hierarchy, we can generate salads that exhibit challenging inference problems. Story salads give rise to a novel, challenging clustering task, where the objective is to group sentences from the same narratives. We demonstrate that simple bag-of-words similarity clustering falls short on this task, and that it is necessary to take into account global context and coherence.</abstract>
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%0 Conference Proceedings
%T Picking Apart Story Salads
%A Wang, Su
%A Holgate, Eric
%A Durrett, Greg
%A Erk, Katrin
%Y Riloff, Ellen
%Y Chiang, David
%Y Hockenmaier, Julia
%Y Tsujii, Jun’ichi
%S Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
%D 2018
%8 oct nov
%I Association for Computational Linguistics
%C Brussels, Belgium
%F wang-etal-2018-picking
%X During natural disasters and conflicts, information about what happened is often confusing and messy, and distributed across many sources. We would like to be able to automatically identify relevant information and assemble it into coherent narratives of what happened. To make this task accessible to neural models, we introduce Story Salads, mixtures of multiple documents that can be generated at scale. By exploiting the Wikipedia hierarchy, we can generate salads that exhibit challenging inference problems. Story salads give rise to a novel, challenging clustering task, where the objective is to group sentences from the same narratives. We demonstrate that simple bag-of-words similarity clustering falls short on this task, and that it is necessary to take into account global context and coherence.
%R 10.18653/v1/D18-1175
%U https://aclanthology.org/D18-1175
%U https://doi.org/10.18653/v1/D18-1175
%P 1455-1465
Markdown (Informal)
[Picking Apart Story Salads](https://aclanthology.org/D18-1175) (Wang et al., EMNLP 2018)
ACL
- Su Wang, Eric Holgate, Greg Durrett, and Katrin Erk. 2018. Picking Apart Story Salads. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 1455–1465, Brussels, Belgium. Association for Computational Linguistics.