KhyontekAI at CheckThat! 2026: Multilingual Dense Retrieval and Cross-Encoder Reranking for Scientific Web Claims

Key points

• Scientific claims shared through social media platforms frequently reference research findings without explicitly linking to the corresponding scientific publications.1
• Automatically retrieving the correct scientific source for such claims is a challenging multilingual retrieval problem involving semantic matching, paraphrasing, noisy social media text, and cross-domain language variation.1
• Our system combines fine-tuned dense retrieval models with cross-encoder reranking for improving multilingual scientific document retrieval.1
• We further investigate multilingual joint training, retrieval depth analysis, metadata enrichment, and language balancing strategies for improving retrieval effectiveness.1

Sources (1)

• [1]KhyontekAI at CheckThat! 2026: Multilingual Dense Retrieval and Cross-Encoder Reranking for Scientific Web Claims
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 11, 06:39 PM

  In this work, we present a multilingual two-stage retrieval framework for CLEF 2026 CheckThat!

  Scientific claims shared through social media platforms frequently reference research findings without explicitly linking to the corresponding scientific publications.

Extractive summary: sentences quoted from the sources.