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. |
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| Challenge: | Existing studies for named entity recognition focus on flat NER, i.e., without nested entities, by sequence labeling methods. |
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