NMIXX: Domain-Adapted Neural Embeddings for Cross-Lingual eXploration of Finance
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Abstract (arXiv)
Financial text embeddings must distinguish changes in event status, perspective, and obligations even when passages share similar wording. NMIXX adapts existing encoders through 18.8k source-linked triplets: paraphrases and Korean-English translations preserve meaning, while targeted financial rewrites introduce semantic contrasts. We examine this recipe across seven backbones on English and Korean financial and general-domain semantic textual similarity (STS), and analyze the composition and passage lengths of KorFinSTS. BGE-M3 attains the highest adapted financial correlations in this comparison, improving from 0.1969 to 0.2967 on FinSTS and from 0.0512 to 0.2732 on KorFinSTS. Its general English and Korean correlations decrease by 0.0391 and 0.0463. Across the seven models, five improve their mean financial correlation, but all reduce their mean general-domain correlation. Per-language comparisons and benchmark-weight sensitivity analysis reveal differences obscured by a single aggregate score. The study contributes a finance-specific supervision design and evidence for evaluating adaptation jointly with retained general semantic capability; direct cross-language retrieval remains outside its evaluation scope.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 12 of its 18 references that are in the map. First posted on arXiv 13 Jul 2025.
Builds on
12 of 18 references.
References · 12
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- 2021SimCSE: Simple Contrastive Learning of Sentence Embeddingscited by 2,173
- 2023Prometheus: Inducing Fine-grained Evaluation Capability in Language Modelscited by 575
- 2024Multilingual E5 Text Embeddings: A Technical Reportcited by 372
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- 2022WHEN FLUE MEETS FLANG: Benchmarks and Large Pre-trained Language Model for Financial Domaincited by 117
- 2021TSDAE: Using Transformer-based Sequential Denoising Auto-Encoder for Unsupervised Sentence Embedding Learningcited by 116
- 2025FinMTEB: Finance Massive Text Embedding Benchmarkcited by 18
- 2024Beyond Surface Similarity: Detecting Subtle Semantic Shifts in Financial Narrativescited by 13
- 2025TWICE: What Advantages Can Low-Resource Domain-Specific Embedding Model Bring? -- A Case Study on Korea Financial Textscited by 3
- 2025Domain Adaptation for Japanese Sentence Embeddings with Contrastive Learning based on Synthetic Sentence Generationcited by 1
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