Papers with story endings
A Simple and Effective Approach to the Story Cloze Test (N18-2)
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| Challenge: | Existing approaches to the Cloze Test that use feature engineering to achieve high accuracy are ignoring the training set and training a model on the validation set. |
| Approach: | They propose a fully-neural approach to the Cloze Test using skip-thought embeddings of the stories in a feed-forward network that achieves close to state-of-the-art performance without any feature engineering. |
| Outcome: | The proposed approach achieves close to state-of-the-art performance on the Cloze Test without any feature engineering. |
IgSEG: Image-guided Story Ending Generation (2021.findings-acl)
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| Challenge: | Existing tasks such as story ending generation generate text-based story endings, but visual storytelling generates photo-streams-based stories. |
| Approach: | They propose a task called Image-guided Story Ending Generation (IgSEG) given a multi-sentence story plot and an ending-related image, they propose MGCL to solve these challenges. |
| Outcome: | The proposed model outperforms baselines on automatic and human evaluation. |
Learning to Control the Fine-grained Sentiment for Story Ending Generation (P19-1)
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| Challenge: | Existing studies focus on controlling the sentiment of story endings. |
| Approach: | They propose a generic and novel framework which controls fine-grained sentiment intensity for automatic story ending generation without manually annotating sentiment labels. |
| Outcome: | The proposed framework can generate story endings which meet the given sentiment intensity better. |