Papers by Hiroaki Yamada
Analyzing Interpretability of Summarization Model with Eye-gaze Information (2024.lrec-main)
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| Challenge: | Existing studies have provided saliency scores for neural summarization models . eye-gaze information is often used as a proxy for human attention in reading tasks . |
| Approach: | They propose to compare model saliency to human eye-gaze data to determine whether it conforms to human gaze during summarization. |
| Outcome: | The proposed framework compares the model behavior to human summarization performance. |
Cross-domain Analysis on Japanese Legal Pretrained Language Models (2022.findings-aacl)
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| Challenge: | Existing studies do not care the performance of domain-adapted PLMs for a generic domain. |
| Approach: | They propose to use pretraining strategies to build pretrained language models specialised in the legal domain to improve their performance. |
| Outcome: | The pretrained language models can learn domain-specific and general word meanings simultaneously and can distinguish them. |
Annotation Study of Japanese Judgments on Tort for Legal Judgment Prediction with Rationales (2022.lrec-1)
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| Challenge: | An annotation scheme for Japanese judgment documents is proposed to provide a reliable dataset for Legal Judgment Prediction (LJP) the anticipated cost of LJP will be much lower than that of human legal professionals. |
| Approach: | They propose to build an annotation scheme for legal judgment prediction, especially for torts, which extracts decisions and rationales at character-level. |
| Outcome: | The proposed annotation scheme can produce a dataset of Japanese LJP at reasonable reliability. |
Automating Idea Unit Segmentation and Alignment for Assessing Reading Comprehension via Summary Protocol Analysis (2022.lrec-1)
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| Challenge: | In second language learning, summaries are among the most popular type of student assignments. |
| Approach: | They propose to revise the annotation guidelines to allow machine implementation of the new annotation guidelines. |
| Outcome: | The proposed algorithm achieves 0.789 precision and 0.844 recall over the L2WS 2021 corpus. |