The accurate translation of domain-specific terminology remains a critical bottleneck for ensuring the fidelity and professionalism of machine translation systems. This paper presents our submission to the WMT26 Terminology Translation Shared Task: the TaT (Terminology-aware Translation) system. Built upon a structured, pipelined architecture, TaT integrates five specialized components designed to seamlessly ingest, process, and translate ambiguous textual inputs while strictly adhering to targeted terminology dictionary. Specifically, the framework comprises a sentence splitter, a terminology retriever, a lexical disambiguator, a baseline translator, and a dedicated terminology extractor tailored for Track 2 constraints. Both internal benchmarking and ablation studies demonstrate that our decoupled pipeline delivers robust performance, successfully meeting design expectations and substantially mitigating terminology omission and misalignment errors in domain-specific translations.

By learning the human post-edits, the automatic post-editing (APE) models are often used to modify the output of the machine translation (MT) system to make it as close as possible to human translation. We introduce the system used in our submission of WMT’22 Automatic Post-Editing (APE) English-Marathi (En-Mr) shared task. In this task, we first train the MT system of En-Mr to generate additional machine-translation sentences. Then we use the additional triple to bulid our APE model and use APE dataset to further fine-tuning. Inspired by the mixture of experts (MoE), we use GMM algorithm to roughly divide the text of APE dataset into three categories. After that, the experts are added to the APE model and different domain data are sent to different experts. Finally, we ensemble the models to get better performance. Our APE system significantly improves the translations of provided MT results by -2.848 and +3.74 on the development dataset in terms of TER and BLEU, respectively. Finally, the TER and BLEU scores are improved by -1.22 and +2.41 respectively on the blind test set.