Papers by Nobuhiro Kaji
Conversation Initiation by Diverse News Contents Introduction (N19-1)
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| Challenge: | Existing conversation systems assume that the user always initiates conversation and focus on how to respond to the given user’s utterance. |
| Approach: | They propose to generate initial utterance by summarizing and chatting about news articles to avoid boredom by relying on boilerplate utterrances like greetings. |
| Outcome: | The proposed model outperforms baseline models and based on information retrieval based and generation based models. |
Lattice Path Edit Distance: A Romanization-aware Edit Distance for Extracting Misspelling-Correction Pairs from Japanese Search Query Logs (2023.emnlp-industry)
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| Challenge: | Existing methods to extract misspelling-correction pairs from Japanese query logs are not effective due to the unique input methods. |
| Approach: | They propose a romanization-aware edit distance that utilizes romanization lattices to efficiently consider all possible romanized forms of input strings. |
| Outcome: | Empirical results show lattice path edit distance outperforms standard edit distance in Japanese . latticae path editing distance outpersforms existing methods even with romanization . |
Search Query Embeddings via User-behavior-driven Contrastive Learning (2025.naacl-industry)
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Sosuke Nishikawa, Jun Hirako, Nobuhiro Kaji, Koki Watanabe, Hiroki Asano, Souta Yamashiro, Shumpei Sano
| Challenge: | Existing approaches to embed search queries are limited due to shortness and surface-level variations. |
| Approach: | They propose a user-behavior-driven contrastive learning approach which directly aligns query embeddings according to user intent. |
| Outcome: | The proposed model outperforms state-of-the-art text embedding models on real-world QU tasks while minimizing lexical similarities. |
Just One is Enough: An Existence-based Alignment Check for Robust Japanese Pronunciation Estimation (2025.emnlp-industry)
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| Challenge: | Existence-based alignment has been used to detect pronunciation errors in Japanese NLP, but finding reliable attention heads remains challenging. |
| Approach: | They propose a method that detects and corrects pronunciation errors in Japanese by using beam search. |
| Outcome: | The proposed method reduces hallucinations and omissions and improves pronunciation estimation by over 2.5%. |