Papers by Hideo Kobayashi
Bridging Resolution: A Survey of the State of the Art (2020.coling-main)
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| Challenge: | bridging resolution is an anaphora resolution task that is less studied than entity coreference resolution. |
| Approach: | This paper presents a survey of the current state of research on bridging resolution . it identifies and resolves bridling/associative anaphors, which are anamorphic references to non-identical associated antecedents. |
| Outcome: | The proposed task is more difficult than entity coreference resolution because of the lack of annotated corpora and lack of standardized evaluation protocols. |
You Only Read Once (YORO): Learning to Internalize Database Knowledge for Text-to-SQL (2025.naacl-long)
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Hideo Kobayashi, Wuwei Lan, Peng Shi, Shuaichen Chang, Jiang Guo, Henghui Zhu, Zhiguo Wang, Patrick Ng
| Challenge: | Existing text-to-SQL systems encode the same schema for every question, resulting in unnecessary high inference cost and missing crucial database knowledge. |
| Approach: | They propose a paradigm that directly internalizes database knowledge into the parametric knowledge of a text-to-SQL model during training and eliminates the need for schema encoding during inference. |
| Outcome: | The proposed paradigm significantly reduces the input token length by 66%-98% and outperforms traditional systems on three benchmarks. |
PairSpanBERT: An Enhanced Language Model for Bridging Resolution (2023.acl-long)
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| Challenge: | bridging resolution is crucial for machine comprehension of discourse entities for various downstream applications. |
| Approach: | They propose a SpanBERT-based pre-trained model specialized for bridging resolution. |
| Outcome: | The proposed model achieves the best results on three evaluation datasets for bridging resolution despite the noise inherent in the automatically generated data . |
Bridging Resolution: Making Sense of the State of the Art (2021.naacl-main)
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| Challenge: | bridging resolution is a task that involves identifying and resolving bridling/associative anaphors, which are anamorphic references to non-identical associated antecedents. |
| Approach: | They propose a hybrid rule-based and MTL approach that would enable a better understanding of their comparative strengths and weaknesses. |
| Outcome: | The proposed model would be able to better understand their strengths and weaknesses and perform a manual analysis of the errors made by the model. |
End-to-End Neural Bridging Resolution (2022.coling-1)
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| Challenge: | state-of-the-art resolvers for bridging resolution are weaker than entity coreference resolution. |
| Approach: | They evaluate bridging resolvers in an end-to-end setting and strengthen them with better encoders . they also try to gain a better understanding of them through perturbation experiments . |
| Outcome: | bridging resolvers are evaluated in an end-to-end setting and strengthened with better encoders . bribridging resolution is the task of identifying briating anaphors and linking them to their antecedents - a paper by the journal bribing resolution argues . |
Constrained Multi-Task Learning for Bridging Resolution (2022.acl-long)
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| Challenge: | bridging resolution is the task of recognizing and resolving bridling anaphors in a text. |
| Approach: | They propose a constrained multi-task learning framework for bridging resolution that exploits cross-task consistency constraints to guide the learning process and pre-train the entity coreference model on publicly available coreference data. |
| Outcome: | The proposed model achieves state-of-the-art on three standard evaluation corpora. |
What Really Matters for Table LLMs? A Meta-Evaluation of Model and Data Effects (2026.findings-eacl)
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Naihao Deng, Sheng Zhang, Henghui Zhu, Shuaichen Chang, Jiani Zhang, Alexander Hanbo Li, Chung-Wei Hang, Hideo Kobayashi, Yiqun Hu, Patrick Ng
| Challenge: | a series of paradigm shifts have come with distinct characteristics and challenges associated with table modeling. |
| Approach: | They propose to replicate four table LLMs by instruction-tuning three foundation models on four existing datasets. |
| Outcome: | The results show that base model choice plays a more dominant role than training data itself. |