Nasrin Mostafazadeh, Aditya Kalyanpur, Lori Moon, David Buchanan, Lauren Berkowitz, Or Biran, Jennifer Chu-Carroll
| Challenge: | Existing knowledge resources and pretrained language models do not include or readily predict GLUCOSE’s rich inferential content. |
| Approach: | They propose a platform for crowdsourcing GLUCOSE data at scale that uses semi-structured templates to elicit causal explanations. |
| Outcome: | The proposed model can be trained on human-readable stories and build similar models on unseen stories. |
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| Challenge: | Using a crowdsourced corpus of 25,000 event phrases, we construct a new task that uses commonsense reasoning to reason about the likely intents and reactions of the event participants. |
| Approach: | They construct a crowdsourced corpus of 25,000 event phrases and use them to construct 'commonsense inference' they demonstrate that neural encoder-decoder models can compose embedding representations of previously unseen events and reason about the likely intents and reactions of the event participants. |
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Kai Xiong, Xiao Ding, Yixin Cao, Yuxiong Yan, Li Du, Yufei Zhang, Jinglong Gao, Jiaqian Liu, Bing Qin, Ting Liu
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A Method for Building a Commonsense Inference Dataset based on Basic Events (2020.emnlp-main)
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| Challenge: | Existing approaches to acquire commonsense are limited by the general-purpose language models. |
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Explain Yourself! Leveraging Language Models for Commonsense Reasoning (P19-1)
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| Challenge: | Empirical results indicate that we can effectively leverage language models for commonsense reasoning. |
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| Challenge: | Large language models are capable of generating fluent-appearing text with little task-specific supervision. |
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Modeling Naive Psychology of Characters in Simple Commonsense Stories (P18-1)
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| Challenge: | Understanding a narrative requires reasoning about the causal links between the events in the story and the mental states of the characters, even when those relationships are not explicitly stated. |
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Event Causality Is Key to Computational Story Understanding (2024.naacl-long)
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| Challenge: | Cognitive science and symbolic AI research suggest that event causality provides vital information for story understanding. |
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Causal Explanation Analysis on Social Media (D18-1)
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| Challenge: | Understanding causal explanations is an important psychological factor linked to physical and mental health. |
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Zhaowei Wang, Quyet V. Do, Hongming Zhang, Jiayao Zhang, Weiqi Wang, Tianqing Fang, Yangqiu Song, Ginny Wong, Simon See
| Challenge: | Existing efforts to detect commonsense causation from the causal inference perspective are inadequate to seize commonsensical causations. |
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CELLO: Causal Evaluation of Large Vision-Language Models (2024.emnlp-main)
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| Challenge: | Recent advances in large vision-language models have improved causal reasoning abilities . however, current models struggle with tasks like causal reasoning . |
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