Papers by Sosuke Nishikawa
EASE: Entity-Aware Contrastive Learning of Sentence Embedding (2022.naacl-main)
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| Challenge: | Existing methods for learning sentence embeddings are fine-tuning general-purpose pretrained models with a particular training supervision. |
| Approach: | They propose a method for learning sentence embeddings via contrastive learning between sentences and related entities. |
| Outcome: | The proposed method outperforms baseline methods in multilingual settings on a variety of tasks. |
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. |
Data Augmentation with Unsupervised Machine Translation Improves the Structural Similarity of Cross-lingual Word Embeddings (2021.acl-srw)
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| Challenge: | Unsupervised cross-lingual word embedding methods learn a linear transformation matrix that maps two monolingual embeddable spaces that are separately trained with monolingual corpora. |
| Approach: | They propose a method that maps two monolingual embedding spaces that are separately trained with monolingual corpora using a pseudo-parallel corpus. |
| Outcome: | The proposed method outperforms other methods given the same amount of data and shows that using a pseudo-parallel corpus makes the source and target corpora (partially) parallel . |