Papers by Toru Nishino
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 . |
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. |
Factual Accuracy is not Enough: Planning Consistent Description Order for Radiology Report Generation (2022.emnlp-main)
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Toru Nishino, Yasuhide Miura, Tomoki Taniguchi, Tomoko Ohkuma, Yuki Suzuki, Shoji Kido, Noriyuki Tomiyama
| Challenge: | Radiology report generation systems can reduce the workload of radiologists by automatically describing the findings in medical images. |
| Approach: | They propose a planning-based radiology report generation system that generates the overall structure of reports as “plans” prior to generating reports that are accurate and consistent in order. |
| Outcome: | The proposed system improves the content order score by 5.1 pt in time series critical scenarios and the clinical factual accuracy F-score by 9.1 p.t. in time-series irrelevant scenarios. |