Barry Haddow · page 2
Papers
- HUME: Human UCCA-Based Evaluation of Machine Translation (2016 · 58 citations)
HUME is a human semantic evaluation measure. Only the source sentence is annotated, with UCCA (a cross-lingual scheme with scenes, participants, centres, elaborators and linkers). Bilingual annotators then judge whether each unit is preserved in the translation. Because the garbled output is never parsed and no… - Retrieval-Augmented Multilingual Knowledge Editing (2024 · 34 citations)
Knowledge editing (KE) lets developers fix or update facts stored in an LLM without full retraining. Earlier work, however, assumed the edit and the later question share one language, usually English. This paper asks how an edit made in one language can carry over to queries in another. The authors propose ReMaKE… - An Expanded Massive Multilingual Dataset for High-Performance Language Technologies (HPLT) (2025 · 23 citations)
Modern LLMs need huge amounts of clean, varied text, yet open multilingual corpora remain scarce and uneven across languages. - Multilingual Retrieval-Augmented Generation for Knowledge-Intensive Task (2025 · 18 citations)
Most retrieval-augmented generation (RAG) research assumes English questions and English knowledge sources, so it is unclear how well these pipelines work for questions in other languages. - Improving Multilingual Retrieval-Augmented Language Models through Dialectic Reasoning Argumentations (2025 · 12 citations)
Retrieval-augmented generation helps LLMs draw on external facts. In multilingual retrieval, however, the returned documents often disagree, are off-topic, or present different national perspectives, and standard RAG pipelines do not judge their quality. The authors propose Dialectic-RAG (D-RAG), which asks the model… - HPLT 3.0: Very Large-Scale Multilingual Resources for LLMs and MT. Mono- and Bi-lingual Data, Multilingual Evaluation, and Pre-Trained Models (2026 · 1 citation)
Most openly released web-scale pre-training corpora come from large companies and are dominated by English.
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