arXiv:2602. 17663v3 Announce Type: replace Abstract: HIPE-2026 is a CLEF evaluation lab dedicated to person-place relation extraction from noisy, multilingual historical texts.
By Juri Opitz, Corina Racl\'e, Emanuela Boros, Andrianos Michail, Matteo Romanello, Maud Ehrmann, Simon Clematide
arXiv:2606. 31325v1 Announce Type: new Abstract: We present HistoriQA-ThirdRepublic: a French-language dataset of multi-hop historical questions derived from parliamentary debates and newspapers of the French Third Republic.
By Aur\'elien Pellet (LRE), Julien Perez (EPITA, LRE), Marie Puren (LRE, CJM)
The paper introduces CLAW-4L, a benchmark of 300 pairs of English and non‑English Wikipedia biographies (French, Chinese, Azerbaijani) focused on women from non‑English contexts, complete with claim annotations and a fine‑grained claim‑pair relation corpus. It proposes a claim‑based enrichment framework that extracts claims from both biographies, aligns them to identify enrichment evidence from the non‑English version, and rewrites the English biography accordingly. Experiments demonstrate that non‑English Wikipedia biographies can improve English biography coverage, though lower‑resource settings still pose challenges.
By Yifei Song, Ziyang Chen, Emil Sayilov, Claire Gardent
The paper introduces Multi-Negative Direct Preference Optimisation (MDPO), a pairwise objective that compares the correct entity with all valid rejected candidates for each mention, extending the single-negative approach used in prior work. MDPO retains the Bradley‑Terry formulation of Direct Preference Optimisation while leveraging the full candidate set through masked, length‑normalised sequence scores. Experiments on French, German, English, Swedish, and Finnish historical newspaper datasets (hipe‑2020 and newseye) show that MDPO outperforms both supervised fine‑tuning and single‑negative DPO, especially for NIL mentions, semantic ambiguity, OCR noise, and historically challenging names, and highlight candidate retrieval as a key bottleneck.
By Tien Nam Nguyen, Emanuela Boros, Ahmed Hamdi, Adam Jatowt, Micka\"el Coustaty, Antoine Doucet
Event analysis is an essential and fundamental direction of information extraction, involving various event-centric tasks at different granularity of documents. While large language models (LLMs) have preliminarily achieved promising performance in part of these tasks individually, their capability in event analysis still lacks comprehensive understanding due to restricted document granularity, task designs, and data source of existing benchmarks.
TRACE is a training‑free, agentic retrieval framework that enables accountable source discovery in historical archives, addressing challenges such as OCR degradation and genre heterogeneity. Developed for the DECIDON project on French Third Republic political discourse, it is deployed internally for 24 researchers across six institutions. On the HistoriQA‑ThirdRepublic benchmark, TRACE achieves R@10 of 0.856 and MRR of 0.653, outperforming sparse, dense, graph‑based, and other agentic RAG baselines, especially on multi‑hop and cross‑corpus questions, while costing only about $0.02 per question.
By Donghan Bian (ENC, LRE), Marie Puren (LRE, ENC), Florian Cafiero (LRE, ENC)