The paper introduces QwenSub-MT, a system that is context-aware and constraint-controlled and has been developed for the WMT 2026 Video Subtitle Translation shared task. It carried out the translation of Simplified Chinese subtitles into 6 different languages. QwenSub-MT used Qwen3.5-Instruct together with a multistage 4-bit QLoRA adaptation and treated adjacent subtitle cues as contextual blocks. In order to enhance consistency and disambiguation, the system incorporated video synopsis information, previous translations, terminology memory and selective visual grounding. A revision stage was carried out to correct semantic, terminology and formatting errors, while duration-aware length control, line-break optimization and deterministic SRT validation were used to make sure that the outputs were readable and structurally valid. 500 subtitle files were submitted in five language directions and obtained a macro-average score of 58.459, placing sixth in the overall ranking. The results show that contextual modelling and structural control are useful, but candidate diversity, independent quality estimation and stronger adaptation to the target language still needs improvement.

This paper presents HEMANT framework, the BSVLP submission to the WMT 2026 Low-Resource Arabic–Asian Machine Translation Shared Task. The system addresses ten bidirectional translation directions involving Arabic, English, Hindi, Bangla, Indonesian, and Urdu. HEMANT integrates language-specific Unicode normalization, spelling correction, named-entity recognition, knowledge-base-assisted entity translation, transliteration, multilingual transfer learning, and parameter-efficient adaptation of the NLLB-200 model using LoRA. Two directional multilingual models were trained for many-to-Arabic and Arabic-to-many translations. The official results showed considerable variation across language pairs. Arabic–Hindi produced the strongest relative performance, while English–Arabic remained the most challenging pair. In several directions, COMET scores were more competitive than BLEU and TER, suggesting that semantic adequacy was often better preserved than lexical overlap. The findings highlight both the potential and limitations of entity-aware multilingual adaptation for low-resource Arabic–Asian translation.