Machine translation based on large language models (LLMs) has achieved remarkable progress in recent years. However, relying on a single translation model and fixed translation instructions often limits translation quality, especially for multilingual and multi-domain scenarios. To address this issue, we propose HW-TSC-Agent, a multi-stage translation agent framework that integrates translation model fine-tuning, dual-model candidate generation, and instruction-aware translation refinement into a unified pipeline. First, we perform Supervised Fine-Tuning (SFT) and Contrastive Preference Optimization (CPO) on openPangu-Embedded-7B using high-quality bilingual data to enhance its multilingual translation capabilities. The fine-tuned openPangu-Embedded-7B and HY-MT2-7B are then used to generate candidate translations. Finally, source-language analysis, candidate translations, and task-specific instructions are incorporated into a dynamic prompt, which guides DeepSeek-V4-Flash to compare, refine, and fuse the candidate translations to produce the final output. Our system is submitted to the WMT2026 General Machine Translation Shared Task, covering 13 translation directions. On the WMT2025 English-to-Chinese development test set, enriching the dynamic prompt generally improves translation quality over an instruction-only DeepSeek-V4-Flash baseline.

We describe HW-TSC’s submissions to both tracks and all language pairs of the WMT26 Terminology Translation Shared Task. Our primary system uses Qwen3.7-Max for source-term extraction, bilingual term alignment, and document translation. In Track 1, it matches the current document or translation chunk against the supplied dictionary and places at most 200 applicable term pairs in the translation prompt. In Track 2, it first induces a bilingual glossary from the seed bitexts and then reuses the Track 1 translation module. Induction combines open source-term extraction with seed-source expression matching, followed by LLM alignment to the seed targets. We optimize the extraction prompt with SkillOpt on a train/validation/test split of the WMT25 English–Chinese finance data and use Qwen3.7-Max to rewrite it for directions without labeled optimization data. Extraction recall rises from 0.6250 to 0.6344 on validation and from 0.6451 to 0.6674 on the held-out test split. On the same held-out test documents with Qwen3.7-Max, glossary induction raises TSR from 0.5212 (no glossary) to 0.7910 with the initial prompt and to 0.8026 after SkillOpt, with chrF++ of 57.45, 67.05, and 67.36; the official glossary yields TSR 0.9019 and chrF++ 63.94.

This paper describes the HW-TSC’s submission to the IWSLT 2026 Offline Speech Translation Task, specifically for the English-to-Chinese and English-to-German unconstrained tracks. Our system adopts a robust cascade architecture optimized for long-form, unsegmented audio. To mitigate the hallucination and inconsistency issues common in long-sequence processing, we propose a two-pass transcription strategy: an initial streaming ASR with a 12-second context buffer for sentence-level coherence, followed by Qwen3-ForcedAligner for precise timestamping. Based on these alignments, a second-pass refinement is conducted using Qwen3-Omni on re-segmented 30-second chunks to ensure high-fidelity transcriptions. For the translation module, we employ a context-aware segment merging strategy (up to 150 tokens) to empower the Qwen3 llm with sufficient semantic context. Experimental results on the tst-2022 benchmark demonstrate the effectiveness of our pipeline, achieving COMET scores of 0.8462 (En-Zh) and 0.7854 (En-De), significantly outperforming the standard cascade baselines.

This paper presents HW-TSC’s submission to the IWSLT 2026 Cross-Lingual Voice Cloning Track. The Cross-Lingual Voice Cloning Track includes three target languages: Arabic, Chinese, and French. We take part in two language tasks of this track, namely Chinese and French. We employ the Qwen3-TTS-12Hz-1.7B-Base multilingual model as the core voice cloning model. To tackle problems such as excessively long duration of the original reference audio and scattered features, we design a sliding-window audio segmentation preprocessing method, which continuously splits long audio into standardized short segments with overlapping redundancy. This method avoids feature attenuation caused by overly long audio and maximizes the preservation of complete timbre information through step overlap. To select the outputs with the highest timbre similarity from numerous synthetic results, this study conducts voiceprint recognition based on the Enhanced Context-Dependent Adversarial Time Delay Neural Network (ECAPA-TDNN), with cosine similarity as the core quantitative evaluation metric, and selects the result with the highest similarity as the optimal output.

This paper introduces HW-TSC’s submission to the IWSLT 2026 Subtitling track. For automatic subtitle generation, we employ a cascaded strategy under unconstrained conditions. First, we construct a large-model-based streaming speech recognition framework, which incorporates VAD voice activity detection, sliding-window context caching, long audio chunking, and the Qwen3 forced alignment model to achieve high-precision transcription and timestamping from English speech to text. Next, we perform text translation using a Qwen3-based translation model. Finally, according to subtitle constraints such as characters per second (CPS) and characters per line (CPL), we identify translation segments that exceed compliance thresholds via quantitative evaluation, and rewrite them using a large language model while preserving core semantic meaning, ultimately producing subtitle files that meet the required standards.