Papers by Kosuke Akimoto
Low-resource Taxonomy Enrichment with Pretrained Language Models (2021.emnlp-main)
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| Challenge: | Taxonomies represent hierarchical relationships between terms or entities. |
| Approach: | They propose a framework for taxonomy enrichment in low-resource settings with pretrained language models as knowledge bases to compensate for the shortage of information. |
| Outcome: | The proposed framework predicts whether inputted term pairs have hierarchical relationships and leverages implicit knowledge from the LM to generate queries efficiently. |
Cross-Sentence N-ary Relation Extraction using Lower-Arity Universal Schemas (D19-1)
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| Challenge: | Existing approaches to extract n-ary relations from text are limited to binary relations. |
| Approach: | They propose to learn relation representations of lower-arity facts from decomposing higher-arities . they conduct experiments with datasets for ternary relation extraction . |
| Outcome: | The proposed method improves the performance of n-ary relation extraction methods compared to previous methods. |
Context Quality Matters in Training Fusion-in-Decoder for Extractive Open-Domain Question Answering (2023.findings-emnlp)
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| Challenge: | Existing studies have shown that the quantity and quality of context affect retrieval-augmented generation models during training. |
| Approach: | They propose a method to mitigate overfitting to specific context quality by introducing bias to the cross-attention distribution. |
| Outcome: | The proposed method improves retrieval-augmented generation models on different context quality. |