2024
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HW-TSC’s Speech to Text Translation System for IWSLT 2024 in Indic track
Bin Wei
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Zongyao Li
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Jiaxin Guo
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Daimeng Wei
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Zhanglin Wu
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Xiaoyu Chen
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Zhiqiang Rao
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Shaojun Li
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Yuanchang Luo
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Hengchao Shang
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Hao Yang
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Yanfei Jiang
Proceedings of the 21st International Conference on Spoken Language Translation (IWSLT 2024)
This article introduces the process of HW-TSC and the results of IWSLT 2024 Indic Track Speech to Text Translation. We designed a cascade system consisting of an ASR model and a machine translation model to translate speech from one language to another. For the ASR part, we directly use whisper large v3 as our ASR model. Our main task is to optimize the machine translation model (en2ta, en2hi, en2bn). In the process of optimizing the translation model, we first use bilingual corpus to train the baseline model. Then we use monolingual data to construct pseudo-corpus data to further enhance the baseline model. Finally, we filter the parallel corpus data through the labse filtering method and finetune the model again, which can further improve the bleu value. We also selected domain data from bilingual corpus to finetune previous model to achieve the best results.
2023
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Improving Neural Machine Translation Formality Control with Domain Adaptation and Reranking-based Transductive Learning
Zhanglin Wu
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Zongyao Li
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Daimeng Wei
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Hengchao Shang
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Jiaxin Guo
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Xiaoyu Chen
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Zhiqiang Rao
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Zhengzhe Yu
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Jinlong Yang
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Shaojun Li
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Yuhao Xie
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Bin Wei
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Jiawei Zheng
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Ming Zhu
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Lizhi Lei
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Hao Yang
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Yanfei Jiang
Proceedings of the 20th International Conference on Spoken Language Translation (IWSLT 2023)
This paper presents Huawei Translation Service Center (HW-TSC)’s submission on the IWSLT 2023 formality control task, which provides two training scenarios: supervised and zero-shot, each containing two language pairs, and sets constrained and unconstrained conditions. We train the formality control models for these four language pairs under these two conditions respectively, and submit the corresponding translation results. Our efforts are divided into two fronts: enhancing general translation quality and improving formality control capability. According to the different requirements of the formality control task, we use a multi-stage pre-training method to train a bilingual or multilingual neural machine translation (NMT) model as the basic model, which can improve the general translation quality of the base model to a relatively high level. Then, under the premise of affecting the general translation quality of the basic model as little as possible, we adopt domain adaptation and reranking-based transductive learning methods to improve the formality control capability of the model.
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Treating General MT Shared Task as a Multi-Domain Adaptation Problem: HW-TSC’s Submission to the WMT23 General MT Shared Task
Zhanglin Wu
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Daimeng Wei
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Zongyao Li
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Zhengzhe Yu
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Shaojun Li
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Xiaoyu Chen
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Hengchao Shang
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Jiaxin Guo
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Yuhao Xie
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Lizhi Lei
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Hao Yang
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Yanfei Jiang
Proceedings of the Eighth Conference on Machine Translation
This paper presents the submission of Huawei Translate Services Center (HW-TSC) to the WMT23 general machine translation (MT) shared task, in which we participate in Chinese↔English (zh↔en) language pair. We use Transformer architecture and obtain the best performance via a variant with larger parameter size. We perform fine-grained pre-processing and filtering on the provided large-scale bilingual and monolingual datasets. We mainly use model enhancement strategies, including Regularized Dropout, Bidirectional Training, Data Diversification, Forward Translation, Back Translation, Alternated Training, Curriculum Learning and Transductive Ensemble Learning. Our submissions obtain competitive results in the final evaluation.
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Multifaceted Challenge Set for Evaluating Machine Translation Performance
Xiaoyu Chen
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Daimeng Wei
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Zhanglin Wu
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Ting Zhu
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Hengchao Shang
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Zongyao Li
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Jiaxin Guo
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Ning Xie
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Lizhi Lei
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Hao Yang
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Yanfei Jiang
Proceedings of the Eighth Conference on Machine Translation
Machine Translation Evaluation is critical to Machine Translation research, as the evaluation results reflect the effectiveness of training strategies. As a result, a fair and efficient evaluation method is necessary. Many researchers have raised questions about currently available evaluation metrics from various perspectives, and propose suggestions accordingly. However, to our knowledge, few researchers has analyzed the difficulty level of source sentence and its influence on evaluation results. This paper presents HW-TSC’s submission to the WMT23 MT Test Suites shared task. We propose a systematic approach for construing challenge sets from four aspects: word difficulty, length difficulty, grammar difficulty and model learning difficulty. We open-source two Multifaceted Challenge Sets for Zh→En and En→Zh. We also present results of participants in this year’s General MT shared task on our test sets.
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The Path to Continuous Domain Adaptation Improvements by HW-TSC for the WMT23 Biomedical Translation Shared Task
Zhanglin Wu
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Daimeng Wei
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Zongyao Li
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Zhengzhe Yu
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Shaojun Li
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Xiaoyu Chen
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Hengchao Shang
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Jiaxin Guo
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Yuhao Xie
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Lizhi Lei
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Hao Yang
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Yanfei Jiang
Proceedings of the Eighth Conference on Machine Translation
This paper presents the domain adaptation methods adopted by Huawei Translation Service Center (HW-TSC) to train the neural machine translation (NMT) system on the English↔German (en↔de) language pair of the WMT23 biomedical translation task. Our NMT system is built on deep Transformer with larger parameter sizes. Based on the biomedical NMT system trained last year, we leverage Curriculum Learning, Data Diversification, Forward translation, Back translation, and Transductive Ensemble Learning to further improve system performance. Overall, we believe our submission can achieve highly competitive result in the official final evaluation.
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HW-TSC’s Submissions to the WMT23 Discourse-Level Literary Translation Shared Task
Yuhao Xie
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Zongyao Li
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Zhanglin Wu
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Daimeng Wei
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Xiaoyu Chen
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Zhiqiang Rao
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Shaojun Li
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Hengchao Shang
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Jiaxin Guo
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Lizhi Lei
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Hao Yang
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Yanfei Jiang
Proceedings of the Eighth Conference on Machine Translation
This paper introduces HW-TSC’s submission to the WMT23 Discourse-Level Literary Translation shared task. We use standard sentence-level transformer as a baseline, and perform domain adaptation and discourse modeling to enhance discourse-level capabilities. Regarding domain adaptation, we employ Back-Translation, Forward-Translation and Data Diversification. For discourse modeling, we apply strategies such as Multi-resolutional Document-to-Document Translation and TrAining Data Augmentation.
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Empowering a Metric with LLM-assisted Named Entity Annotation: HW-TSC’s Submission to the WMT23 Metrics Shared Task
Zhanglin Wu
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Yilun Liu
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Min Zhang
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Xiaofeng Zhao
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Junhao Zhu
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Ming Zhu
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Xiaosong Qiao
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Jingfei Zhang
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Ma Miaomiao
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Zhao Yanqing
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Song Peng
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Shimin Tao
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Hao Yang
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Yanfei Jiang
Proceedings of the Eighth Conference on Machine Translation
This paper presents the submission of Huawei Translation Service Center (HW-TSC) to the WMT23 metrics shared task, in which we submit two metrics: KG-BERTScore and HWTSC-EE-Metric. Among them, KG-BERTScore is our primary submission for the reference-free metric, which can provide both segment-level and system-level scoring. While HWTSC-EE-Metric is our primary submission for the reference-based metric, which can only provide system-level scoring. Overall, our metrics show relatively high correlations with MQM scores on the metrics tasks of previous years. Especially on system-level scoring tasks, our metrics achieve new state-of-the-art in many language pairs.
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Leveraging Multilingual Knowledge Graph to Boost Domain-specific Entity Translation of ChatGPT
Min Zhang
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Limin Liu
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Zhao Yanqing
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Xiaosong Qiao
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Su Chang
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Xiaofeng Zhao
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Junhao Zhu
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Ming Zhu
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Song Peng
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Yinglu Li
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Yilun Liu
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Wenbing Ma
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Mengyao Piao
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Shimin Tao
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Hao Yang
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Yanfei Jiang
Proceedings of Machine Translation Summit XIX, Vol. 2: Users Track
Recently, ChatGPT has shown promising results for Machine Translation (MT) in general domains and is becoming a new paradigm for translation. In this paper, we focus on how to apply ChatGPT to domain-specific translation and propose to leverage Multilingual Knowledge Graph (MKG) to help ChatGPT improve the domain entity translation quality. To achieve this, we extract the bilingual entity pairs from MKG for the domain entities that are recognized from source sentences. We then introduce these pairs into translation prompts, instructing ChatGPT to use the correct translations of the domain entities. To evaluate the novel MKG method for ChatGPT, we conduct comparative experiments on three Chinese-English (zh-en) test datasets constructed from three specific domains, of which one domain is from biomedical science, and the other two are from the Information and Communications Technology (ICT) industry — Visible Light Communication (VLC) and wireless domains. Experimental results demonstrate that both the overall translation quality of ChatGPT (+6.21, +3.13 and +11.25 in BLEU scores) and the translation accuracy of domain entities (+43.2%, +30.2% and +37.9% absolute points) are significantly improved with MKG on the three test datasets.
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KG-IQES: An Interpretable Quality Estimation System for Machine Translation Based on Knowledge Graph
Junhao Zhu
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Min Zhang
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Hao Yang
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Song Peng
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Zhanglin Wu
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Yanfei Jiang
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Xijun Qiu
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Weiqiang Pan
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Ming Zhu
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Ma Miaomiao
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Weidong Zhang
Proceedings of Machine Translation Summit XIX, Vol. 2: Users Track
The widespread use of machine translation (MT) has driven the need for effective automatic quality estimation (AQE) methods. How to enhance the interpretability of MT output quality estimation is well worth exploring in the industry. From the perspective of the alignment of named entities (NEs) in the source and translated sentences, we construct a multilingual knowledge graph (KG) consisting of domain-specific NEs, and design a KG-based interpretable quality estimation (QE) system for machine translations (KG-IQES). KG-IQES effectively estimates the translation quality without relying on reference translations. Its effectiveness has been verified in our business scenarios.