用双编码器和神经重排序，提升社交媒体隐含科学主张的文献检索准确率。
AIRwaves at CheckThat! 2025: Retrieving Scientific Sources for Implicit Claims on Social Media with Dual Encoders and Neural Re-Ranking
- 双阶段检索：先用E5-large双编码器粗筛，再用SciBERT交叉编码器精排。
- 最终在盲测集上达到MRR@5=0.6828，显著优于基线的0.5025。
- 适合关注社交媒体事实核查与科研证据匹配的研究者和开发者。
将社交媒体中隐含的科学主张与其原始文献关联，对基于证据的事实核查和学术对话至关重要，但受制于词汇稀疏、查询极短及领域语言特性。团队AIRwaves在CLEF-2025 CheckThat! Lab的子任务4b中排名第二，提出一种证据检索方法，显著超越基准。优化后的稀疏检索基线（BM25）在金标准盲测集上MRR@5为0.5025。为超越此基线，引入两阶段检索流程：(i) 第一阶段使用基于E5-large的双编码器，通过批内及挖掘困难负样本微调，并结合分块分词与丰富文档元数据；(ii) 第二阶段采用SciBERT交叉编码器进行神经重排序。以神经表示替代纯词法匹配后，性能提升至MRR@5=0.6174，完整流水线进一步提升至MRR@5=0.6828。结果表明，稠密检索与神经重排序结合可有效解决推文到研究论文的匹配问题，为未来证据检索系统提供实用范式。
原文摘要 · Abstract (English)
Linking implicit scientific claims made on social media to their original publications is crucial for evidence-based fact-checking and scholarly discourse, yet it is hindered by lexical sparsity, very short queries, and domain-specific language. Team AIRwaves ranked second in Subtask 4b of the CLEF-2025 CheckThat! Lab with an evidence-retrieval approach that markedly outperforms the competition baseline. The optimized sparse-retrieval baseline(BM25) achieves MRR@5 = 0.5025 on the gold label blind test set. To surpass this baseline, a two-stage retrieval pipeline is introduced: (i) a first stage that uses a dual encoder based on E5-large, fine-tuned using in-batch and mined hard negatives and enhanced through chunked tokenization and rich document metadata; and (ii) a neural re-ranking stage using a SciBERT cross-encoder. Replacing purely lexical matching with neural representations lifts performance to MRR@5 = 0.6174, and the complete pipeline further improves to MRR@5 = 0.6828. The findings demonstrate that coupling dense retrieval with neural re-rankers delivers a powerful and efficient solution for tweet-to-study matching and provides a practical blueprint for future evidence-retrieval pipelines.
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