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        <title>A Multi-Attention based Neural Network with External Knowledge for Story Ending Predicting Task</title>
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    <abstract>Enabling a mechanism to understand a temporal story and predict its ending is an interesting issue that has attracted considerable attention, as in case of the ROC Story Cloze Task (SCT). In this paper, we develop a multi-attention-based neural network (MANN) with well-designed optimizations, like Highway Network, and concatenated features with embedding representations into the hierarchical neural network model. Considering the particulars of the specific task, we thoughtfully extend MANN with external knowledge resources, exceeding state-of-the-art results obviously. Furthermore, we develop a thorough understanding of our model through a careful hand analysis on a subset of the stories. We identify what traits of MANN contribute to its outperformance and how external knowledge is obtained in such an ending prediction task.</abstract>
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%0 Conference Proceedings
%T A Multi-Attention based Neural Network with External Knowledge for Story Ending Predicting Task
%A Li, Qian
%A Li, Ziwei
%A Wei, Jin-Mao
%A Gu, Yanhui
%A Jatowt, Adam
%A Yang, Zhenglu
%Y Bender, Emily M.
%Y Derczynski, Leon
%Y Isabelle, Pierre
%S Proceedings of the 27th International Conference on Computational Linguistics
%D 2018
%8 August
%I Association for Computational Linguistics
%C Santa Fe, New Mexico, USA
%F li-etal-2018-multi
%X Enabling a mechanism to understand a temporal story and predict its ending is an interesting issue that has attracted considerable attention, as in case of the ROC Story Cloze Task (SCT). In this paper, we develop a multi-attention-based neural network (MANN) with well-designed optimizations, like Highway Network, and concatenated features with embedding representations into the hierarchical neural network model. Considering the particulars of the specific task, we thoughtfully extend MANN with external knowledge resources, exceeding state-of-the-art results obviously. Furthermore, we develop a thorough understanding of our model through a careful hand analysis on a subset of the stories. We identify what traits of MANN contribute to its outperformance and how external knowledge is obtained in such an ending prediction task.
%U https://aclanthology.org/C18-1149
%P 1754-1762

Markdown (Informal)

[A Multi-Attention based Neural Network with External Knowledge for Story Ending Predicting Task](https://aclanthology.org/C18-1149) (Li et al., COLING 2018)

• A Multi-Attention based Neural Network with External Knowledge for Story Ending Predicting Task (Li et al., COLING 2018)

ACL

• Qian Li, Ziwei Li, Jin-Mao Wei, Yanhui Gu, Adam Jatowt, and Zhenglu Yang. 2018. A Multi-Attention based Neural Network with External Knowledge for Story Ending Predicting Task. In Proceedings of the 27th International Conference on Computational Linguistics, pages 1754–1762, Santa Fe, New Mexico, USA. Association for Computational Linguistics.