Japanese SimCSE Technical Report
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Abstract (arXiv)
We report the development of Japanese SimCSE, Japanese sentence embedding models fine-tuned with SimCSE. Since there is a lack of sentence embedding models for Japanese that can be used as a baseline in sentence embedding research, we conducted extensive experiments on Japanese sentence embeddings involving 24 pre-trained Japanese or multilingual language models, five supervised datasets, and four unsupervised datasets. In this report, we provide the detailed training setup for Japanese SimCSE and their evaluation results.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 10 of its 32 references that are in the map. First posted on arXiv 30 Oct 2023.
Builds on
10 of 32 references.
References · 10
- 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
- 2019Unsupervised Cross-lingual Representation Learning at Scalecited by 3,935
- 2017A Broad-Coverage Challenge Corpus for Sentence Understanding through Inferencecited by 2,682
- 2019Optuna: A Next-generation Hyperparameter Optimization Frameworkcited by 2,501
- 2021SimCSE: Simple Contrastive Learning of Sentence Embeddingscited by 2,173
- 2015A large annotated corpus for learning natural language inferencecited by 1,847
- 2017SemEval-2017 Task 1: Semantic Textual Similarity - Multilingual and Cross-lingual Focused Evaluationcited by 1,001
- 2020LUKE: Deep Contextualized Entity Representations with Entity-aware Self-attentioncited by 326
- 2022DiffCSE: Difference-based Contrastive Learning for Sentence Embeddingscited by 132
- 2022Compositional Evaluation on Japanese Textual Entailment and Similaritycited by 14
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1 paper.