Papers by Shotaro Misawa

2 papers
Keeping Consistency of Sentence Generation and Document Classification with Multi-Task Learning (D19-1)

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Challenge: Existing automated generation of articles' characteristics is inconsistent if they are generated individually.
Approach: They propose a multi-task learning model with a shared encoder and multiple decoders for each task.
Outcome: The proposed model generates more consistent headlines, key phrases and categories . it outperforms baseline model on the ROUGE scores and generates fluent headlines .
Integrating Tree Structures and Graph Structures with Neural Networks to Classify Discussion Discourse Acts (C18-1)

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Challenge: Existing models that analyze textual contents and discussion structures require understanding of textual content and discussion structure.
Approach: They propose a model that integrates discussion structures with neural networks to classify discourse acts.
Outcome: The proposed model improves accuracy and FB1 score by 1.5% compared to the previous best model.

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