Papers by Ryuji Kano
Reinforcement Learning with Imbalanced Dataset for Data-to-Text Medical Report Generation (2020.findings-emnlp)
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Toru Nishino, Ryota Ozaki, Yohei Momoki, Tomoki Taniguchi, Ryuji Kano, Norihisa Nakano, Yuki Tagawa, Motoki Taniguchi, Tomoko Ohkuma, Keigo Nakamura
| Challenge: | Medical datasets are imbalanced in their finding labels because incidence rates differ among diseases . authors propose a novel reinforcement learning method with a reconstructor to improve clinical correctness of generated reports. |
| Approach: | They propose a reinforcement learning method with a reconstructor to improve clinical correctness of generated reports. |
| Outcome: | The proposed method improves clinical correctness of generated reports . it also trains the model on infrequent findings . |
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 . |
Harnessing Popularity in Social Media for Extractive Summarization of Online Conversations (D18-1)
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| Challenge: | Existing methods for summarizing online conversations require large amounts of training data. |
| Approach: | They propose a disjunctive model that computes the contribution of content and context separately. |
| Outcome: | The proposed model outperforms baseline models which use popularity as informativeness measure. |
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. |
Identifying Implicit Quotes for Unsupervised Extractive Summarization of Conversations (2020.aacl-main)
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| Challenge: | Existing methods of unsupervised summarization are lacking. |
| Approach: | They propose an unsupervised unsupervised extractive neural summarization model that extracts quotes as summaries from conversational texts. |
| Outcome: | The proposed model can extract quoted sentences as summaries from two email and social media datasets. |
Quantifying Appropriateness of Summarization Data for Curriculum Learning (2021.eacl-main)
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| Challenge: | Summarization datasets are noisy, and summaries often do not reflect what is written in the source texts. |
| Approach: | They propose a method of curriculum learning to train summarization models from noisy data. |
| Outcome: | The proposed method improves the performance of pretrained and non-pretrained models on human evaluation. |