Papers by Ryuto Konno
Pseudo Zero Pronoun Resolution Improves Zero Anaphora Resolution (2021.emnlp-main)
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| Challenge: | Masked language models have contributed to drastic performance improvements with regard to zero anaphora resolution (ZAR). |
| Approach: | They propose a pretraining task that trains MLMs on anaphoric relations with explicit supervision and a finetuning method that remedies a notorious discrepancy. |
| Outcome: | The proposed method improves zero anaphora resolution in Japanese ZAR . it uses a pretrain task and finetuning task to correct the discrepancy . |
Instance-Based Learning of Span Representations: A Case Study through Named Entity Recognition (2020.acl-main)
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Hiroki Ouchi, Jun Suzuki, Sosuke Kobayashi, Sho Yokoi, Tatsuki Kuribayashi, Ryuto Konno, Kentaro Inui
| Challenge: | Recent neural networks can induce good span feature representations and achieve high performance in structured prediction tasks. |
| Approach: | They propose an instance-based learning method that learns similarities between spans . they aim to build models that have high interpretability without sacrificing performance . |
| Outcome: | The proposed method improves interpretability without sacrificing performance. |
An Empirical Study of Contextual Data Augmentation for Japanese Zero Anaphora Resolution (2020.coling-main)
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| Challenge: | Existing methods to augment labeled data are limited by the scarcity of labeles . a method called contextual data augmentation (CDA) can be used to augment labels . |
| Approach: | They propose a data augmentation method that generates labeled training instances using a pretrained language model. |
| Outcome: | The proposed method can improve the quality of augmented training data compared to the conventional method. |