wmt26

The University of Melbourne submits a domain-balanced machine translation model for low-resource Pacific creoles like Tok Pisin, Bislama, and Solomon Pijin, outperforming open baselines by over 3 chrF++ points in the WMT 2026 CreoleMT shared task.

This paper describes six submissions to the WMT26 Model Compression Shared Task, using routing-informed expert pruning and MXFP4 quantization to compress GPT-OSS-20B into smaller translation models with parameters ranging from 4.186B to 7.770B.

This paper presents a retrieval-augmented translation system using BM25 and Gemini 2.5 Flash for low-resource North-Eastern Indian languages, submitted to the WMT26 shared task without model fine-tuning.