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NMIXX: Domain-Adapted Neural Embeddings for Cross-Lingual eXploration of Finance

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Listed:
  • Hanwool Lee
  • Sara Yu
  • Yewon Hwang
  • Jonghyun Choi
  • Heejae Ahn
  • Sungbum Jung
  • Youngjae Yu

Abstract

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.

Suggested Citation

  • Hanwool Lee & Sara Yu & Yewon Hwang & Jonghyun Choi & Heejae Ahn & Sungbum Jung & Youngjae Yu, 2025. "NMIXX: Domain-Adapted Neural Embeddings for Cross-Lingual eXploration of Finance," Papers 2507.09601, arXiv.org, revised Sep 2026.
    • Handle: RePEc:arx:papers:2507.09601
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    References listed on IDEAS

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    1. Guijin Son & Hanwool Lee & Nahyeon Kang & Moonjeong Hahm, 2023. "Removing Non-Stationary Knowledge From Pre-Trained Language Models for Entity-Level Sentiment Classification in Finance," Papers 2301.03136, arXiv.org, revised Jan 2023.
    2. Nhu Khoa Nguyen & Thierry Delahaut & Emanuela Boros & Antoine Doucet & Gael Lejeune, 2023. "Contextualizing Emerging Trends in Financial News Articles," Papers 2301.11318, arXiv.org.
    3. Yewon Hwang & Sungbum Jung & Hanwool Lee & Sara Yu, 2025. "TWICE: What Advantages Can Low-Resource Domain-Specific Embedding Model Bring? -- A Case Study on Korea Financial Texts," Papers 2502.07131, arXiv.org, revised Apr 2025.
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    1. Yewon Hwang & Sungbum Jung & Hanwool Lee & Sara Yu, 2025. "TWICE: What Advantages Can Low-Resource Domain-Specific Embedding Model Bring? -- A Case Study on Korea Financial Texts," Papers 2502.07131, arXiv.org, revised Apr 2025.

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