HW - TSC ’s Submissions to the WMT 2025 Segment-level Quality Score Prediction Task (2025)

Abstract: This paper presents the submissions of Huawei Translate Services Center (HW-TSC) to the WMT 2025 Segment-level quality score prediction Task. We participate in 16 language pairs. For the prediction of translation quality scores for long multi-sentence text units, we propose an automatic evaluation framework based on alignment algorithms. Our approach integrates sentence segmentation tools and dynamic programming to construct sentence-level alignments between source and translated texts, then adapts sentence-level evaluation models to document-level assessment via sliding-window aggregation. Our submissions achieved competitive results in the final evaluations of all language pairs we participated in.

## introduction recent advances in large language models (llms) @xcite @xcite have opened new possibilities for documentlevel machine translation (doc-mt) @xcite @xcite . leveraging their robust language generation capabilities and profound contextual understanding, llms can produce translations that are more natural, fluent, and semantically coherent. these models have demonstrated remarkable proficiency in processing long-form texts, thereby significantly enhancing the quality of document-level translation. however, this approach also introduces several challenges. since llms translate entire documents holistically rather than processing sentences sequentially, the output may suffer from issues such as over-translation (excessive paraphrasing) or under-translation (omissions). furthermore, the absence of sentence-level alignment between source and target texts-combined with the inherent length of both-makes it difficult to assess * these authors contributed equally to this work. translation quality accurately. robust evaluation methods for document-level machine translation (mt) remain an unresolved critical problem. while human evaluation remains the gold standard for assessing translation quality due to its nuanced understanding of language and context, it faces inherent limitations in scalability, subjectivity, and cost-efficiency, particularly for largescale document-level translation tasks. automated metrics like bertscore @xcite and comet @xcite , though capable of capturing semantic nuances and demonstrating strong correlation with human judgments, are constrained by input length restrictions and their reliance on sentence-level alignment between source and reference texts. while @xcite pioneered the adaptation of these metrics to document-level translation evaluation, its applicability remains severely constrained by its fundamental requirement for perfect sentence-level alignment among source texts, translations, and reference translations. this strict one-to-one correspondence prerequisite significantly limits its practical utility in real-world scenarios where such ideal alignments rarely exist. recent attempts to leverage large language models (llms) as evaluators through carefully designed prompts show promising alignment with professional human assessments across multiple dimensions including accuracy, fluency, and stylistic consistency @xcite . however, these methods suffer from high computational costs, sensitivity to training data biases, and instability across different prompts or model runs, raising concerns about their reliability and reproducibility for practical applications. in this work, we employ an innovative alignment algorithm to automatically construct sentencelevel alignment between source and translated texts. our approach @xcite involves: (1) sentence segmentation of source and target texts, (2) alignment metric computation, (3) anchoring of source text segmentation information, and (4) reconstructed target text segmentation (including merging and gap filling). by subsequently applying sliding-window-based sentence-level evaluation, we achieve document-level assessment effectiveness, thereby successfully adapting sentence-level pretrained model evaluation methods to document translation. ## results we participated in all language pair competitions within the segment-level quality score prediction task, which included a total of 16 language pairs. after the alignment phase, we obtained new sentence-level text pairs corresponding to each paragraph text pair. at this point, we conducted respective predictions using wmt22cometkiwi-da(asd-kiwi), wmt23-cometkiwi-daxl(asd-kiwi-xl), and wmt23-cometkiwi-daxxl(asd-kiwi-xxl), with the results shown in to leverage both models, we propose an ensemble method that averages the per-sentence scores of asd-kiwi and asd-kiwi-xl. empirical results confirm that asd-kiwi-ensemble achieves the best overall performance. ## conclusion this paper presents the methodology behind hw-tsc's submission to the wmt 2025 segment-level quality score prediction task. our approach integrates sentence segmentation tools and dynamic programming algorithms to construct sentence-level alignments between source and translated texts, then adapts sentence-level evaluation models to document-level assessment through sliding-window aggregation. by incorporating an ensemble strategy, our method achieved the highest correlation scores across all 16 languages in this task.