Papers with SCT
Tackling the Story Ending Biases in The Story Cloze Test (P18-2)
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| Challenge: | Story Cloze Test (SCT) is a recent framework for evaluating story comprehension and script learning. |
| Approach: | They propose to use a crowdsourcing scheme to create a new SCT dataset to overcome some of the biases discovered in the original SCT. |
| Outcome: | The proposed model performs better than the baselines on the SCT dataset, despite human-authorship biases. |
ArT: All-round Thinker for Unsupervised Commonsense Question Answering (2022.coling-1)
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| Challenge: | Existing work on commonsense QA requires labeled training data for its success . existing work relies on large-scale in-domain or out-of-domain labeles or fails to generate knowledge of high quality in a general way. |
| Approach: | They propose an approach to commonsense question-answering (QA) that takes association during knowledge generation. |
| Outcome: | The proposed model outperforms existing models on commonsense QA benchmarks. |
A Multi-Attention based Neural Network with External Knowledge for Story Ending Predicting Task (C18-1)
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| Challenge: | Existing studies on the topic of common sense story understanding focus on generating guesses for a missing event or concentrating on unsupervised learning. |
| Approach: | They propose to extend attention-based neural network with external knowledge resources to understand temporal stories and predict their endings. |
| Outcome: | The proposed model outperforms state-of-the-art models and external knowledge resources. |
Scene Restoring for Narrative Machine Reading Comprehension (2020.emnlp-main)
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| Challenge: | Narrative passages describe a chain of events, which helps the machine understand the passage comprehensively. |
| Approach: | They propose a method to let machine read narrative passages with their prior knowledge . they build a scene graph using Atomic as external knowledge and encode it with GDIN . |
| Outcome: | The proposed method achieves state-of-the-art on a Story Cloze Test and CosmosQA datasets. |
A Corpus for Commonsense Inference in Story Cloze Test (2022.lrec-1)
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| Challenge: | Story Cloze Test (SOTA) models can achieve over 90% accuracy on predicting the last sentence, but high accuracy can be achieved by merely using surface-level features. |
| Approach: | They constructed a human-labeled and human-verified commonsense knowledge inference dataset using data from 1871 stories and three human workers labeled each story. |
| Outcome: | The proposed models can achieve 90% accuracy on predicting the last sentence, but they don't perform well on new and more challenging tasks. |
GenPT: Beyond Self-Report for Reliable LLM Psychometrics via Generative Projective Testing (2026.acl-long)
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Ming Wang, Shuang Wu, Bixuan Wang, Lu Lin, Yuxin Chen, Xiaocui Yang, Daling Wang, Shi Feng, Yifei Zhang, Yufan Sun
| Challenge: | Large language models (LLMs) inherit contamination from training corpora, directional bias under social-desirability framing, and limited responsiveness to context beyond the item text. |
| Approach: | They propose a paradigm that reformulates TAT, Rorschach, and SCT with newly generated stimuli and organises assessment as a three-stage pipeline. |
| Outcome: | The proposed paradigm reformulates TAT, Rorschach, and SCT with newly generated stimuli and organises assessment as a three-stage pipeline. |