We present the findings of the WMT26 Shared Task on Automated Translation Quality Evaluation Systems, continuing last year’s unification of the earlier separate WMT Metrics and Quality Estimation shared tasks. This year we evaluated three complementary views of segment-level translation quality on a common test set: fine-grained error-span detection, continuous quality-score prediction, and a new task on identifying error-free translations. Submissions to upstream tasks were also converted automatically to downstream predictions. The evaluation covered 21 translation directions using human cESA judgments from the WMT26 General Machine Translation task, with optional reference translations generated as either native, post-edited, or pseudo-references, depending on the translation direction. Official evaluation data was complemented by five submitted challenge sets. Across all three primary tasks, unsupervised LLM-as-a-judge approaches outperformed all traditional and supervised metrics, while open-weight models such as Gemma 4 were found to be largely competitive with proprietary frontier models. Reference-free LLM judges are highly competitive, and the benefit of adding a reference largely depends on its provenance and on the evaluator model family.

High-quality International Phonetic Alphabet (IPA) transcriptions are a foundational resource for speech and language technologies, yet existing tools for many low-resource languages remain limited in accuracy and scope. In this work, we present MekongPhon, a large-scale, high-quality parallel IPA corpus for Lao and Khmer. The corpus contains 1.3 million Khmer and 367 thousand Lao orthographic–IPA pairs, meticulously aligned and verified. When used to train Transformer-based sequence-to-sequence models, MekongPhon enables exceptionally accurate IPA generation, achieving under 2% Character Error Rate (CER) on held-out test sets. We further introduce linguistically informed Lao and Khmer transliteration tools that offer high-speed IPA conversion, outperforming Epitran by 6-71 CER points despite trading some accuracy for efficiency. All data, code, and pretrained models are publicly released to support future research and development in low-resource language technologies.

Individuals with speech disabilities rely on everyday technologies powered by Automatic Speech Recognition (ASR) systems, yet these systems consistently fail them–producing significantly higher error rates that undermine the usefulness of voice assistants, hands-free devices, and machine translation pipelines. We conduct a multi-stage evaluation of speech impairment effects in cascaded speech-to-text translation, examining two distinct conditions: real dysarthric speech and simulated rhotacism. For dysarthria, we quantify ASR error rates. For rhotacism, we quantify error rates from a minimal-pair text substitution. We then analyze how impairment-induced errors propagate through downstream machine translation across three language directions (English to Spanish, Ukrainian, Khmer), and propose a training-free LLM-based post-correction methodology as an accessible intervention. We find that larger LLMs (70B parameters or more) consistently improve downstream translation quality even when surface-level corrections made by those LLMs remain modest, while smaller models lack the capacity to do so reliably. These results reveal a promising but scale-dependent path toward more equitable speech technology for users with atypical speech.

Multilingual Neural Machine Translation (MNMT) models enhance translation quality for low-resource languages by exploiting cross-lingual similarities during training—a process known as knowledge transfer. This transfer is particularly effective between languages that share lexical or structural features, often enabled by a common orthography. However, languages with strong phonetic and lexical similarities but distinct writing systems experience limited benefits, as the absence of a shared orthography hinders knowledge transfer. To address this limitation, we propose an approach based on phonetic information that enhances token-level alignment across scripts by leveraging transliterations. We systematically evaluate several phonetic transcription techniques and strategies for incorporating phonetic information into NMT models. Our results show that using a shared encoder to process orthographic and phonetic inputs separately consistently yields the best performance for Khmer, Thai, and Lao in both directions with English, and that our custom Cognate-Aware Transliteration (CAT) method consistently improves translation quality over the baseline.

Language tagging, a method whereby source and target inputs are prefixed with a unique language token, has become the de facto standard for conditioning Multilingual Neural Machine Translation (MNMT) models on specific language directions. This conditioning can manifest effective zero-shot translation abilities in MT models at scale for many languages. Expanding on previous work, we propose a novel method of language tagging for MNMT, injection, in which the embedded representation of a language token is concatenated to the input of every linear layer. We explore a variety of different tagging methods, with and without injection, showing that injection improves zero-shot translation performance with up to a 2+ BLEU score point gain for certain language directions in our dataset.

A majority of language technologies are tailored for a small number of high-resource languages, while relatively many low-resource languages are neglected. One such group, Creole languages, have long been marginalized in academic study, though their speakers could benefit from machine translation (MT). These languages are predominantly used in much of Latin America, Africa and the Caribbean. We present the largest cumulative dataset to date for Creole language MT, including 14.5M unique Creole sentences with parallel translations—11.6M of which we release publicly, and the largest bitexts gathered to date for 41 languages—the first ever for 21. In addition, we provide MT models supporting all 41 Creole languages in 172 translation directions. Given our diverse dataset, we produce a model for Creole language MT exposed to more genre diversity then ever before, which outperforms a genre-specific Creole MT model on its own benchmark for 23 of 34 translation directions.

We propose an approach that improves the performance of VMT (Video-guided Machine Translation) models, which integrate text and video modalities. We experiment with the MAD (Movie Audio Descriptions) dataset, a new dataset which contains transcribed audio descriptions of movies. We find that the MAD dataset is more lexically rich than the VATEX dataset (the current VMT baseline), and we experiment with MAD pretraining to improve performance on the VATEX dataset. We experiment with two different video encoder architectures: a Conformer (Convolution-augmented Transformer) and a Transformer. Additionally, we conduct experiments by masking the source sentences to assess the degree to which the performance of both architectures improves due to pretraining on additional video data. Finally, we conduct an analysis of the transfer learning potential of a video dataset and compare it to pretraining on a text-only dataset. Our findings demonstrate that pretraining with a lexically rich dataset leads to significant improvements in model performance when models use both text and video modalities.