Papers by Martin Semmann

5 papers
POLAR: A Benchmark for Multilingual, Multicultural, and Multi-Event Online Polarization (2026.findings-acl)

Copied to clipboard

Challenge: polarization is a pervasive threat to democratic institutions, civil discourse, and social cohesion worldwide . most existing datasets focus on English or high-resource languages, reflecting a widespread trend across NLP tasks .
Approach: They propose a multilingual, multicultural, and multi-event dataset with over 110K instances in 22 languages drawn from diverse online platforms and real-world events.
Outcome: The proposed dataset analyzes polarization detection, type, and manifestation using a variety of annotation platforms adapted to each cultural context.
T2-RAGBench: Text-and-Table Benchmark for Evaluating Retrieval-Augmented Generation (2026.eacl-long)

Copied to clipboard

Challenge: Existing QA datasets containing text-and-table data typically contain context-dependent questions, which may yield multiple correct answers depending on the provided context.
Approach: They propose a benchmark to evaluate RAG methods on text-and-table data.
Outcome: The proposed method evaluates RAG methods on real-world text-and-table data.
HatePRISM: Policies, Platforms, and Research Integration. Advancing NLP for Hate Speech Proactive Mitigation (2025.findings-acl)

Copied to clipboard

Challenge: Existing approaches to manage hate speech rely on reactive measures such as blocking or suspending offensive messages . despite regulations imposed by nations and social media platforms, hateful content remains a challenge .
Approach: They propose a framework for automated hate speech moderation based on different strategies . they examine hate speech regulations and strategies from three perspectives .
Outcome: The proposed framework could be based on a combination of country regulations, social platform policies, and NLP research datasets.
Comprehensive Comparison of RAG Methods Across Multi-Domain Conversational QA (2026.eacl-srw)

Copied to clipboard

Challenge: Existing studies evaluate RAG methods in isolation and focus on single-turn settings.
Approach: They compare retrieval-augmented generation methods for multi-turn conversational QA with those that use dialogue history and coreference to ground large language models.
Outcome: The proposed methods outperform vanilla RAG and advanced methods fail to yield gains and can even degrade performance below the No-RAG baseline.
LEMUR: A Corpus for Robust Fine-Tuning of Multilingual Law Embedding Models for Retrieval (2026.eacl-srw)

Copied to clipboard

Challenge: Existing large language models are not designed for semantic retrieval and PDF-based legislative sources introduce substantial noise due to imperfect text extraction.
Approach: They propose a large-scale multilingual corpus of EU environmental legislation constructed from 24,953 official EUR-Lex PDF documents covering 25 languages.
Outcome: The proposed model improves Top-k retrieval accuracy in monolingual and bilingual settings . it also improves accuracy in low- and high-resource languages .

What is GenGO?

GenGO is an NLP powered publication search system. It currenctly indexes 30k+ papers from ACL Anthology, and implements multi-aspect summarization, semantic search, and more!

Information

About
Limitations