Chinese-Southeast Asian Multilingual MT Task

WMT is a leading conference dedicated to the advancement of machine translation research and technology. The Shared Task is a core component of WMT, providing a standardized platform for researchers to evaluate and compare the performance of different machine translation systems on a common benchmark dataset.

Against the backdrop of the Belt and Road Initiative, the demand for cross-language communication between Chinese and Southeast Asian languages has grown explosively. However, low-resource languages in this region are constrained by limited parallel corpora, weak model generalization, and high barriers to edge deployment. Meanwhile, mainstream high-performance translation models are oversized, making it difficult to meet millisecond-level response requirements for real-time applications and edge devices (e.g., AI phones, IoT terminals).

To address these pain points and promote research on lightweight, efficient, low-resource multilingual machine translation, we launch this dedicated shared task at WMT2026. This task focuses on bidirectional translation between Chinese and seven Southeast Asian languages, with core constraints of small model size, fast inference speed, and strong low-resource language performance. It fills the gap of efficiency-oriented multilingual translation evaluation in WMT, and drives the practical deployment and real-world application of edge-adaptable translation systems.

The primary objectives of this shared task are to: - Encourage advanced research in lightweight, efficient machine translation for low-resource Southeast Asian languages - Provide a unified, standardized benchmark platform for researchers to evaluate and compare translation systems that balance translation quality, model size, and inference speed - Advance the state of the art in edge-deployable multilingual translation for real-time and mobile application scenarios - Establish reproducible benchmarks for low-resource cross-lingual transfer and data-efficient machine translation training

Participants are encouraged to explore and innovate in the following technical directions: - Low-resource data augmentation: Leveraging monolingual corpora to alleviate the scarcity of parallel data for low-resource languages - Lightweight model design: Novel model architectures, parameter-efficient fine-tuning strategies, and knowledge distillation methods tailored for low-resource translation - Inference acceleration: Optimization techniques to achieve low-latency, high-throughput inference on resource-constrained edge devices - Cross-lingual transfer learning: Adapting knowledge from high-resource language pairs to improve low-resource translation performance - Multilingual modeling: Exploring unified multilingual translation frameworks with strong generalization across diverse Southeast Asian languages

All datasets released in this task are collected from copyright-compliant resources including OPUS, Tatoeba, UN Corpus, and self-built manually proofread corpora. The datasets will be open to the research community after registration. WMT participants can use the dataset for non-commercial research purposes in accordance with the CC-BY fair use principle.

We spent 8 months addressing data sourcing and quality control, and invested around 30,000 euros for manual alignment and native speaker verification to build this high-quality benchmark dataset. The detailed data statistics are listed as follows:

In addition to the above datasets, parallel and monolingual data from the WMT2026 General MT Task can also be used for data augmentation in this task.

The held-out blind test set will be released to registered participants at the start of the evaluation cycle. It contains 2,000 unseen sentences for each of the 7 Southeast Asian languages, covering general, news, healthcare, finance and other practical application domains. Each source sentence is paired with exactly one high-quality human reference translation completed and verified by native speakers.

The test set is strictly independent from the training and validation datasets, to ensure the fairness and reliability of the evaluation results. Participants are required to submit the translation outputs of their systems for the test set within the specified evaluation cycle.

The paper submission process will be fully in-line with the official WMT2026 conference requirements and timeline.

We adopt a hybrid automatic evaluation framework that comprehensively assesses both translation quality and model inference efficiency, to fairly compare systems targeting edge deployment and real-time application scenarios.

For any questions about the shared task, please contact the organizing team via official email: WmtEvaluation@163.com

Your system paper submission should be prepared according to the WMT2026 official instructions, and uploaded to the START submission system before the specified deadline (TBD, in line with the WMT2026 main conference schedule).