Papers by Nobuhiro Ueda
J-CRe3: A Japanese Conversation Dataset for Real-world Reference Resolution (2024.lrec-main)
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Nobuhiro Ueda, Hideko Habe, Akishige Yuguchi, Seiya Kawano, Yasutomo Kawanishi, Sadao Kurohashi, Koichiro Yoshino
| Challenge: | Existing studies have ground referential expressions in language to real-world objects for cooperative action generation. |
| Approach: | They propose a Japanese Conversation dataset for real-world reference resolution that ground referential expressions to visual information observed in egocentric views. |
| Outcome: | The proposed dataset contains egocentric video and dialogue audio of real-world conversations between two people acting as a master and assistant robot at home. |
Rapidly Developing High-quality Instruction Data and Evaluation Benchmark for Large Language Models with Minimal Human Effort: A Case Study on Japanese (2024.lrec-main)
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| Challenge: | Recent advances in large language models (LLMs) have aimed to refine their capacity to accurately follow human instructions and navigate intricate scenarios. |
| Approach: | They propose a method that uses a set of instructions to translate English into Japanese and then generates Japanese instruction data using GPT-4. |
| Outcome: | The proposed method outperforms Japanese-Alpaca models in the evaluation benchmarks without human references. |
Disambiguating Reference in Visually Grounded Dialogues through Joint Modeling of Textual and Multimodal Semantic Structures (2025.acl-long)
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| Challenge: | incorporating textual and multimodal reference resolution improves performance in visual-based reference resolution . Phrase grounding is a well-established task for understanding semantic relations between mentions and objects . ambiguities caused by pronouns and ellipses can arise in visually grounded dialogues . |
| Approach: | They propose a framework that unifies textual and multimodal reference resolution by mapping mention embeddings to object embeddements and selecting mentions or objects based on their similarity. |
| Outcome: | The proposed framework performs better in phrase grounding than other models for this task. |
SCAN: Semantic Document Layout Analysis for Textual and Visual Retrieval-Augmented Generation (2026.findings-eacl)
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| Challenge: | Recent research indicates that using VLMs yields better RAG performance, but processing rich documents remains a challenge. |
| Approach: | They propose a VLM-friendly approach that enhances both textual and visual RAG systems. |
| Outcome: | The proposed approach outperforms conventional methods and commercial document processing solutions. |
KWJA: A Unified Japanese Analyzer Based on Foundation Models (2023.acl-demo)
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Nobuhiro Ueda, Kazumasa Omura, Takashi Kodama, Hirokazu Kiyomaru, Yugo Murawaki, Daisuke Kawahara, Sadao Kurohashi
| Challenge: | KWJA supports a wide range of tasks including typo correction, word segmentation, word normalization, named entity recognition, dependency parsing, PAS analysis, bridging reference resolution, coreference resolution, and discourse relation analysis. |
| Approach: | They propose to build a Japanese text analyzer based on foundation models that performs a wide range of tasks. |
| Outcome: | The proposed model performs better in a multi-task manner than other analyzers with specialized models. |
BERT-based Cohesion Analysis of Japanese Texts (2020.coling-main)
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| Challenge: | Recent advances in neural networks have significantly improved natural language processing tasks . they include self training-based language models such as BERT . |
| Approach: | They tackle a systematic analysis of cohesion in Japanese texts using BERT models . they find that coreference resolution is different in nature from other tasks . |
| Outcome: | The proposed analysis outperforms existing studies on cohesion in Japanese texts. |