arXiv AI
BETA-Labeling for Multilingual Dataset Construction in Low-Resource IR
Read the original on arXiv AI →The Flow has not summarised this story yet — read it at arXiv AI.
Related stories
arXiv Computation and Language
Oct 9
Large Language Models for Machine Translation Quality Annotation: Humans and Models Are Both Challenged
Large Language Models (LLMs) are being tested as a cost‑effective alternative to human judgment for evaluating machine translation (MT). This study compares LLM agreement with human annotators on two MT quality schemes—Multidimensional Quality Metrics (MQM) and Error Span Annotation (ESA)—across 70 language pairs and public WMT23/25 data. Results show that while LLMs sometimes match or exceed human agreement, performance varies widely by scheme, language, and domain, and remains unreliable in many settings. The work also identifies specific challenges for both humans (e.g., fine‑grained MQM, low‑resource languages) and LLMs (e.g., minor errors, wrong language variants, span annotation).
arXiv AI
Jul 23
On the Systematic Challenges of Culturally Loaded Machine Translation: Dream of the Red Chamber as the Cultural Lens
arXiv:2607. 20241v1 Announce Type: cross Abstract: Culturally loaded translation poses unique challenges for machine translation (MT), as meanings are deeply embedded in socio-cultural contexts beyond surface linguistic forms.
arXiv Computation and Language
Sep 10
MultiSynt/MT: Trillion-Token Multi-Parallel Pre-Training Data Translated Across 36 Languages
arXiv:2607.00890v2 Announce Type: replace Abstract: Open web-scale pre-training corpora remain concentrated in English, limiting multilingual LLM development. We introduce MultiSynt/MT, an open synth...
arXiv AI
Jun 9
Multilingual Training and Evaluation Resources for Vision-Language Models
arXiv:2604. 18347v2 Announce Type: replace-cross Abstract: Vision Language Models (VLMs) achieved rapid progress in the recent years.
arXiv Computation and Language
Sep 25
EnSiTa - A Trilingual Multi-Domain Parallel Dataset and Benchmark for Domain-Specific Machine Translation
EnSiTa is a trilingual multi‑domain parallel dataset and benchmark for English, Sinhala, and Tamil. It contains human post‑edited training data across seven domains and professionally translated test sets for those domains plus an additional one, all produced through a multi‑year, rigorously quality‑controlled process. The authors use EnSiTa to conduct a comprehensive study of domain‑specific machine translation across six language directions, comparing from‑scratch Transformers, pre‑trained models, and decoder‑only LLMs under various training‑data sizes, model scales, and domain settings.
arXiv Computation and Language
Sep 2
Unified Multi-Dialectal Neural Machine Translation for Bangla Using the Dwadash Benchmark Corpus
arXiv:2608.12018v2 Announce Type: replace Abstract: Neural Machine Translation (NMT) and Large Language Models (LLMs) excel at cross-lingual tasks but often fail to capture intra-lingual morphologica...