Papers by Noriki Nishida
Neural Networks in a Product of Hyperbolic Spaces (2022.naacl-srw)
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| Challenge: | Recent advances in the use of hyperbolic spaces have been reported in natural language processing and graph embedding. |
| Approach: | They propose to extend hyperbolic neural networks to a product of hyperbolical spaces by using a single hyperbolically spaced hyperbole. |
| Outcome: | The proposed method improves graph node classification accuracy on tree-like datasets. |
RNSum: A Large-Scale Dataset for Automatic Release Note Generation via Commit Logs Summarization (2022.acl-long)
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| Challenge: | a release note is a technical document that describes the latest changes to a software product. |
| Approach: | They propose to extract and then abstract release notes from GitHub repositories using a transformer-based network like BART. |
| Outcome: | The proposed methods generate less noisy release notes at higher coverage than baselines. |
Do Multimodal Large Language Models Truly See What We Point At? Investigating Indexical, Iconic, and Symbolic Gesture Comprehension (2025.acl-short)
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| Challenge: | In recent years, multimodal large language models (MLLMs) excel at integrating textual, auditory, and visual information, but their ability to accurately interpret gestures remains underexplored. |
| Approach: | They annotated five gesture type labels to 925 gesture instances from the Miraikan SC Corpus and analyzed gesture descriptions generated by state-of-the-art MLLMs, including GPT-4o. |
| Outcome: | The proposed models lack real-world referential understanding and are inconsistent in interpreting indexical gestures. |
Recent Trends in Personalized Dialogue Generation: A Review of Datasets, Methodologies, and Evaluations (2024.lrec-main)
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| Challenge: | Personalization is a multifaceted process that requires multiple definitions and varies between individuals. |
| Approach: | They propose to systemically survey the recent landscape of personalized dialogue generation including the datasets employed, methodologies developed, and evaluation metrics applied. |
| Outcome: | The proposed model can generate fluent and coherent responses to human queries in a language-based conversational agent. |
Dissecting GraphRAG: A Modular Analysis of Knowledge Structuring for Factoid Question Answering (2026.tacl-1)
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Noriki Nishida, Rumana Ferdous Munne, Shanshan Liu, Narumi Tokunaga, Yuki Yamagata, Fei Cheng, Kouji Kozaki, Yuji Matsumoto
| Challenge: | GraphRAG integrates structured knowledge graphs into question answering . high-quality triple extraction is critical, but lacks granularity and topical coherence . large language models suffer from inherent limitations in their internalized knowledge . |
| Approach: | They evaluate module-level design choices in GraphRAG for retrieval-augmented generation . they find that triple extraction is critical for accurate and comprehensive retrieval . |
| Outcome: | The proposed framework outperforms other retrieval-augmented generation frameworks in accuracy and efficiency. |
Zero-Shot Entailment Learning for Ontology-Based Biomedical Annotation Without Explicit Mentions (2025.coling-main)
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Rumana Ferdous Munne, Noriki Nishida, Shanshan Liu, Narumi Tokunaga, Yuki Yamagata, Kouji Kozaki, Yuji Matsumoto
| Challenge: | Automated biomedical annotation presents significant challenges when entities are not explicitly mentioned in the text. |
| Approach: | They propose an entailment-based zero-shot text classification approach to annotate biomedical text passages using the Homeostasis Imbalance Process (HOIP) ontology. |
| Outcome: | The proposed method performs well when processes are not explicitly mentioned . it is time-consuming and expensive to annotate biomedical texts with a specific ontology . |
MA-COIR: Leveraging Semantic Search Index and Generative Models for Ontology-Driven Biomedical Concept Recognition (2025.acl-srw)
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Shanshan Liu, Noriki Nishida, Rumana Ferdous Munne, Narumi Tokunaga, Yuki Yamagata, Kouji Kozaki, Yuji Matsumoto
| Challenge: | Existing concepts recognition methods that rely on explicit mention identification fail to capture complex concepts not explicitly stated in the text. |
| Approach: | They propose a framework that reformulates concept recognition as an indexing-recognition task. |
| Outcome: | The proposed framework reduces computational requirements and improves recognition efficiency in low-resource settings. |
Out-of-Domain Discourse Dependency Parsing via Bootstrapping: An Empirical Analysis on Its Effectiveness and Limitation (2022.tacl-1)
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| Challenge: | Discourse parsing accuracy degrades significantly on out-of-domain text. |
| Approach: | They propose to use bootstrapping methods to adapt modern discourse dependency parsers to out-of-domain text without additional human supervision. |
| Outcome: | The proposed methods are significantly and consistently effective for unsupervised domain adaptation of discourse dependency parsing, but the low coverage of accurately predicted pseudo labels is a bottleneck for further improvement. |
Applicability Condition Extraction for Therapeutic Drug-Disease Relations (2026.findings-acl)
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| Challenge: | Existing methods for identifying conditions under which a drug can be effective are limited . et al., j. n. d., al. c., and dr. m. s., 2005, are not able to identify context-specific conditions for therapeutic drug–disease relations. |
| Approach: | They propose to annotate triples of drugs, diseases, and applicability conditions from biomedical literature. |
| Outcome: | The proposed method outperforms baselines across evaluation settings. |
Unsupervised Discourse Constituency Parsing Using Viterbi EM (2020.tacl-1)
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| Challenge: | Existing studies on unsupervised discourse parsing have shown that it is expensive, time-consuming, and sometimes highly ambiguous. |
| Approach: | They propose an unsupervised parsing algorithm using Viterbi EM with a margin-based criterion and initialization methods for Viterbia training of discourse constituents based on prior knowledge of text structures. |
| Outcome: | The proposed method outperforms fully supervised parsers in terms of performance and learning of discourse constituents. |
J-Shuwa: A Large-Scale Web-Collected Japanese Sign Language-Japanese Parallel Corpus (2026.findings-acl)
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| Challenge: | Japanese Sign Language (JSL) is a low-resource sign language that has received limited attention in the AI community due to the lack of large-scale, publicly available parallel corpora. |
| Approach: | They propose a large-scale JSL-Japanese parallel corpus constructed from YouTube videos with hard-coded subtitles and closed captions. |
| Outcome: | The proposed model is effective for training models and can be used for future research across a wide range of tasks. |
A Visually-grounded First-person Dialogue Dataset with Verbal and Non-verbal Responses (2020.emnlp-main)
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| Challenge: | In visual-grounded dialogue systems, first-person visual information about where the other speakers are and what they are paying attention to is crucial to understand their intentions. |
| Approach: | They propose a visually-grounded first-person dialogue (VFD) dataset with verbal and non-verbal responses. |
| Outcome: | The proposed dataset provides verbal and non-verbal responses for first-person visual information and recent neural network models. |
Better Generalizing to Unseen Concepts: An Evaluation Framework and An LLM-Based Auto-Labeled Pipeline for Biomedical Concept Recognition (2026.eacl-long)
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Shanshan Liu, Noriki Nishida, Fei Cheng, Narumi Tokunaga, Rumana Ferdous Munne, Yuki Yamagata, Kouji Kozaki, Takehito Utsuro, Yuji Matsumoto
| Challenge: | Existing methods for recognizing ontology concepts are limited by the number of annotations available. |
| Approach: | They propose an evaluation framework built on hierarchical concept indices and novel metrics to measure generalization. |
| Outcome: | The proposed evaluation framework is built on hierarchical concept indices and novel metrics to measure generalization. |
Post Persona Alignment for Multi-Session Dialogue Generation (2025.findings-emnlp)
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| Challenge: | Existing methods for multi-session persona-based dialogue generation typically retrieve persona information before response generation, which can constrain diversity and result in generic outputs. |
| Approach: | They propose a two-stage framework that reverses the process of retrieving persona information before response generation. |
| Outcome: | Experiments on multi-session persona-based dialogue data show that the proposed framework outperforms existing methods in consistency, diversity, and persona relevance. |