Papers by Takumi Takahashi
CLER: Cross-task Learning with Expert Representation to Generalize Reading and Understanding (D19-58)
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| Challenge: | In-domain datasets are used to train and validate our model, and other out-of-domain data are used for validation. |
| Approach: | They propose a model which uses cross-task learning with expert representation for the generalization of reading and understanding. |
| Outcome: | The proposed model achieved an average F1 score of 66.1 % in the out-of-domain setting, which is a 4.3 percentage point improvement over the official BERT baseline model. |
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. |