Papers by Haruki Nagasawa

2 papers
Can LMs Store and Retrieve 1-to-N Relational Knowledge? (2023.acl-srw)

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Challenge: Pretraining language models on large amounts of text has made it difficult to store and retrieve world knowledge.
Approach: They propose to view pretrained language models as knowledge bases by examining their ability to store and retrieve world knowledge.
Outcome: The proposed model can store and retrieve world knowledge with high accuracy, but it is not clear how accurately it can handle 1-to-N relational knowledge.
A Challenging Multimodal Video Summary: Simultaneously Extracting and Generating Keyframe-Caption Pairs from Video (2023.emnlp-main)

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Challenge: Existing methods to summarize video content have only considered video and image data, and the trend towards multimodal video summarization is changing.
Approach: They propose a multimodal video summarization task setting and a dataset to train and evaluate the task.
Outcome: The proposed task is useful as a practical application and presents a highly challenging problem worthy of study.

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