Papers by Nasrin Mostafazadeh
GLUCOSE: GeneraLized and COntextualized Story Explanations (2020.emnlp-main)
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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. |
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. |