Papers by Takashi Shibuya
Good Examples Make A Faster Learner: Simple Demonstration-based Learning for Low-resource NER (2022.acl-long)
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Dong-Ho Lee, Akshen Kadakia, Kangmin Tan, Mahak Agarwal, Xinyu Feng, Takashi Shibuya, Ryosuke Mitani, Toshiyuki Sekiya, Jay Pujara, Xiang Ren
| Challenge: | Recent advances in prompt-based learning have shown strong results on few-shot text classification by using cloze-style templates. |
| Approach: | They propose a demonstration-based learning method which lets the input be prefaced by task demonstrations for in-context learning. |
| Outcome: | The proposed method improves on in-domain learning and domain adaptation in low-resource settings. |
On the Language Encoder of Contrastive Cross-modal Models (2024.findings-acl)
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Mengjie Zhao, Junya Ono, Zhi Zhong, Chieh-Hsin Lai, Yuhta Takida, Naoki Murata, Wei-Hsiang Liao, Takashi Shibuya, Hiromi Wakaki, Yuki Mitsufuji
| Challenge: | Pretrained audio-language models such as AudioCLIP and AudioCLAP have shown promising results on vision-language (VL) tasks. |
| Approach: | They extensively evaluate how unsupervised and supervised sentence embedding training affect language encoder quality and cross-modal task performance. |
| Outcome: | The proposed model improves on visual-language (VL) and audio-language tasks when the amount of training data is large. |
Nested Named Entity Recognition via Second-best Sequence Learning and Decoding (2020.tacl-1)
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| Challenge: | Named entity recognition (NER) is the task of identifying text spans associated with proper names and classifying them according to their semantic class such as person or organization. |
| Approach: | They propose a method that treats the tag sequence for nested entities as the second best path within the span of their parent entity. |
| Outcome: | The proposed method achieves F1-scores of 85.82%, 84.34%, and 77.36% on ACE-2004, ACE 2005, and GENIA datasets. |
XMD: An End-to-End Framework for Interactive Explanation-Based Debugging of NLP Models (2023.acl-demo)
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Dong-Ho Lee, Akshen Kadakia, Brihi Joshi, Aaron Chan, Ziyi Liu, Kiran Narahari, Takashi Shibuya, Ryosuke Mitani, Toshiyuki Sekiya, Jay Pujara, Xiang Ren
| Challenge: | Existing models are susceptible to learning spurious biases that do not reflect the underlying task. |
| Approach: | They propose an open-source framework for explanation-based model debugging that allows users to provide various forms of feedback on model explanations. |
| Outcome: | The proposed framework improves model’s OOD performance on text classification tasks by up to 18%. |