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Story Comprehension for Predicting What Happens Next
Snigdha Chaturvedi
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Haoruo Peng
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Dan Roth
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Paper Details:
Month: September
Year: 2017
Location: Copenhagen, Denmark
Venue:
EMNLP |
Citations
URL
Generating Reasonable and Diversified Story Ending Using Sequence to Sequence Model with Adversarial Training
Zhongyang Li
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Xiao Ding
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Ting Liu
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A Multi-Attention based Neural Network with External Knowledge for Story Ending Predicting Task
Qian Li
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Ziwei Li
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Jin-Mao Wei
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Yanhui Gu
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Adam Jatowt
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Zhenglu Yang
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A Simple and Effective Approach to the Story Cloze Test
Siddarth Srinivasan
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Richa Arora
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Mark Riedl
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A Spatial Model for Extracting and Visualizing Latent Discourse Structure in Text
Shashank Srivastava
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Nebojsa Jojic
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Tackling the Story Ending Biases in The Story Cloze Test
Rishi Sharma
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James Allen
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Omid Bakhshandeh
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Nasrin Mostafazadeh
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No URLs Found
Field Of Study
Linguistic Trends
Discourse
Approach
Bayesian Model
Language
English
Dataset
News
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