Papers with CEF

4 papers
European Language Resource Coordination: Collecting Language Resources for Public Sector Multilingual Information Management (L18-1)

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Challenge: European Language Resource Coordination (ELRC) initiated a number of actions to support the collection of Language Resources (LRs) within the public sector in EU member and CEF-affiliated countries.
Approach: They propose to initiate actions to support the collection of Language Resources (LRs) within the public sector in EU member and CEF-affiliated countries.
Outcome: The European Language Resource Coordination (ELRC) consortium initiated a number of actions to support the collection of Language Resources (LRs) within the public sector in EU member and CEF-affiliated countries.
Assessing Multilinguality of Publicly Accessible Websites (2022.lrec-1)

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Challenge: multilingualism on the Web is a problem not only at the world level, but also at the European and regional level.
Approach: They propose a tool that automatically analyses the language diversity of the Web and propose indicators and methodologies to measure multilingualism of European websites.
Outcome: The proposed tool can be independently run at set intervals and concludes that multilingualism on the Web is still a problem not only at the world level, but also at the European and regional level.
Discovering Parallel Language Resources for Training MT Engines (L18-1)

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Challenge: Web crawling is an efficient way for compiling the monolingual, parallel and/or domain-specific corpora needed for machine translation and other HLT applications.
Approach: They propose a system for compiling monolingual, parallel and/or domain-specific corpora . ILSP-FC is a web crawling system that generates bilingual lexica and terminology lists .
Outcome: The ILSP Focused Crawler is a system developed by researchers at the IL SP/Athena RIC for the acquisition of such resources.
Cross-Examination Framework: A Task-Agnostic Diagnostic for Information Fidelity in Text-to-Text Generation (2026.acl-long)

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Challenge: Traditional metrics like BLEU and BERTScore fail to capture semantic fidelity in generative text-to-text tasks.
Approach: They propose a cross-examination framework that generates verifiable questions from each text and performs a Cross-exam to derive three interpretable scores: Coverage, Conformity, and Consistency.
Outcome: The proposed framework detects critical errors across translation, summarization and clinical note-generation and human expert validation shows it is reliable without gold references.

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