2 citations · 2 across the 3 of their papers we have counts for
3 papers
cs.CL2025
NMIXX: Domain-Adapted Neural Embeddings for Cross-Lingual eXploration of Finance
Hanwool Lee, Sara Yu, Yewon Hwang +4
Financial text embeddings must distinguish changes in event status, perspective, and obligations even when passages share similar wording. NMIXX adapts existing encoders through 18…
cs.CL2025
TWICE: What Advantages Can Low-Resource Domain-Specific Embedding Model Bring? -- A Case Study on Korea Financial Texts
Yewon Hwang, Sungbum Jung, Hanwool Lee +1
Domain specificity of embedding models is critical for effective performance. However, existing benchmarks, such as FinMTEB, are primarily designed for high-resource languages, lea…
cs.CL2023★ 2 cited
EaSyGuide : ESG Issue Identification Framework leveraging Abilities of Generative Large Language Models
Hanwool Lee, Jonghyun Choi, Sohyeon Kwon +1
This paper presents our participation in the FinNLP-2023 shared task on multi-lingual environmental, social, and corporate governance issue identification (ML-ESG). The task's obje…