Zhiqiang Rao


2023

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Length-Aware NMT and Adaptive Duration for Automatic Dubbing
Zhiqiang Rao | Hengchao Shang | Jinlong Yang | Daimeng Wei | Zongyao Li | Jiaxin Guo | Shaojun Li | Zhengzhe Yu | Zhanglin Wu | Yuhao Xie | Bin Wei | Jiawei Zheng | Lizhi Lei | Hao Yang
Proceedings of the 20th International Conference on Spoken Language Translation (IWSLT 2023)

This paper presents the submission of Huawei Translation Services Center for the IWSLT 2023 dubbing task in the unconstrained setting. The proposed solution consists of a Transformer-based machine translation model and a phoneme duration predictor. The Transformer is deep and multiple target-to-source length-ratio class labels are used to control target lengths. The variation predictor in FastSpeech2 is utilized to predict phoneme durations. To optimize the isochrony in dubbing, re-ranking and scaling are performed. The source audio duration is used as a reference to re-rank the translations of different length-ratio labels, and the one with minimum time deviation is preferred. Additionally, the phoneme duration outputs are scaled within a defined threshold to narrow the duration gap with the source audio.

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Improving Neural Machine Translation Formality Control with Domain Adaptation and Reranking-based Transductive Learning
Zhanglin Wu | Zongyao Li | Daimeng Wei | Hengchao Shang | Jiaxin Guo | Xiaoyu Chen | Zhiqiang Rao | Zhengzhe Yu | Jinlong Yang | Shaojun Li | Yuhao Xie | Bin Wei | Jiawei Zheng | Ming Zhu | Lizhi Lei | Hao Yang | 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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HW-TSC at IWSLT2023: Break the Quality Ceiling of Offline Track via Pre-Training and Domain Adaptation
Zongyao Li | Zhanglin Wu | Zhiqiang Rao | Xie YuHao | Guo JiaXin | Daimeng Wei | Hengchao Shang | Wang Minghan | Xiaoyu Chen | Zhengzhe Yu | Li ShaoJun | Lei LiZhi | Hao Yang
Proceedings of the 20th International Conference on Spoken Language Translation (IWSLT 2023)

This paper presents HW-TSC’s submissions to the IWSLT 2023 Offline Speech Translation task, including speech translation of talks from English to German, Chinese, and Japanese, respectively. We participate in all three conditions (constrained training, constrained with large language models training, and unconstrained training) with models of cascaded architectures. We use data enhancement, pre-training models and other means to improve the ASR quality, and R-Drop, deep model, domain data selection, etc. to improve the translation quality. Compared with last year’s best results, we achieve 2.1 BLEU improvement on the MuST-C English-German test set.

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The HW-TSC’s Simultaneous Speech-to-Text Translation System for IWSLT 2023 Evaluation
Jiaxin Guo | Daimeng Wei | Zhanglin Wu | Zongyao Li | Zhiqiang Rao | Minghan Wang | Hengchao Shang | Xiaoyu Chen | Zhengzhe Yu | Shaojun Li | Yuhao Xie | Lizhi Lei | Hao Yang
Proceedings of the 20th International Conference on Spoken Language Translation (IWSLT 2023)

In this paper, we present our submission to the IWSLT 2023 Simultaneous Speech-to-Text Translation competition. Our participation involves three language directions: English-German, English-Chinese, and English-Japanese. Our proposed solution is a cascaded incremental decoding system that comprises an ASR model and an MT model. The ASR model is based on the U2++ architecture and can handle both streaming and offline speech scenarios with ease. Meanwhile, the MT model adopts the Deep-Transformer architecture. To improve performance, we explore methods to generate a confident partial target text output that guides the next MT incremental decoding process. In our experiments, we demonstrate that our simultaneous strategies achieve low latency while maintaining a loss of no more than 2 BLEU points when compared to offline systems.

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The HW-TSC’s Simultaneous Speech-to-Speech Translation System for IWSLT 2023 Evaluation
Hengchao Shang | Zhiqiang Rao | Zongyao Li | Zhanglin Wu | Jiaxin Guo | Minghan Wang | Daimeng Wei | Shaojun Li | Zhengzhe Yu | Xiaoyu Chen | Lizhi Lei | Hao Yang
Proceedings of the 20th International Conference on Spoken Language Translation (IWSLT 2023)

In this paper, we present our submission to the IWSLT 2023 Simultaneous Speech-to-Speech Translation competition. Our participation involves three language directions: English-German, English-Chinese, and English-Japanese. Our solution is a cascaded incremental decoding system, consisting of an ASR model, an MT model, and a TTS model. By adopting the strategies used in the Speech-to-Text track, we have managed to generate a more confident target text for each audio segment input, which can guide the next MT incremental decoding process. Additionally, we have integrated the TTS model to seamlessly reproduce audio files from the translation hypothesis. To enhance the effectiveness of our experiment, we have utilized a range of methods to reduce error conditions in the TTS input text and improve the smoothness of the TTS output audio.

2022

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HW-TSC’s Submissions to the WMT 2022 General Machine Translation Shared Task
Daimeng Wei | Zhiqiang Rao | Zhanglin Wu | Shaojun Li | Yuanchang Luo | Yuhao Xie | Xiaoyu Chen | Hengchao Shang | Zongyao Li | Zhengzhe Yu | Jinlong Yang | Miaomiao Ma | Lizhi Lei | Hao Yang | Ying Qin
Proceedings of the Seventh Conference on Machine Translation (WMT)

This paper presents the submissions of Huawei Translate Services Center (HW-TSC) to the WMT 2022 General Machine Translation Shared Task. We participate in 6 language pairs, including Zh↔En, Ru↔En, Uk↔En, Hr↔En, Uk↔Cs and Liv↔En. We use Transformer architecture and obtain the best performance via multiple variants with larger parameter sizes. We perform fine-grained pre-processing and filtering on the provided large-scale bilingual and monolingual datasets. For medium and highresource languages, we mainly use data augmentation strategies, including Back Translation, Self Training, Ensemble Knowledge Distillation, Multilingual, etc. For low-resource languages such as Liv, we use pre-trained machine translation models, and then continue training with Regularization Dropout (R-Drop). The previous mentioned data augmentation methods are also used. Our submissions obtain competitive results in the final evaluation.

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HW-TSC’s Submission for the WMT22 Efficiency Task
Hengchao Shang | Ting Hu | Daimeng Wei | Zongyao Li | Xianzhi Yu | Jianfei Feng | Ting Zhu | Lizhi Lei | Shimin Tao | Hao Yang | Ying Qin | Jinlong Yang | Zhiqiang Rao | Zhengzhe Yu
Proceedings of the Seventh Conference on Machine Translation (WMT)

This paper presents the submission of Huawei Translation Services Center (HW-TSC) to WMT 2022 Efficiency Shared Task. For this year’s task, we still apply sentence-level distillation strategy to train small models with different configurations. Then, we integrate the average attention mechanism into the lightweight RNN model to pursue more efficient decoding. We tried adding a retrain step to our 8-bit and 4-bit models to achieve a balance between model size and quality. We still use Huawei Noah’s Bolt for INT8 inference and 4-bit storage. Coupled with Bolt’s support for batch inference and multi-core parallel computing, we finally submit models with different configurations to the CPU latency and throughput tracks to explore the Pareto frontiers.

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HW-TSC Translation Systems for the WMT22 Biomedical Translation Task
Zhanglin Wu | Jinlong Yang | Zhiqiang Rao | Zhengzhe Yu | Daimeng Wei | Xiaoyu Chen | Zongyao Li | Hengchao Shang | Shaojun Li | Ming Zhu | Yuanchang Luo | Yuhao Xie | Miaomiao Ma | Ting Zhu | Lizhi Lei | Song Peng | Hao Yang | Ying Qin
Proceedings of the Seventh Conference on Machine Translation (WMT)

This paper describes the translation systems trained by Huawei translation services center (HW-TSC) for the WMT22 biomedical translation task in five language pairs: English↔German (en↔de), English↔French (en↔fr), English↔Chinese (en↔zh), English↔Russian (en↔ru) and Spanish→English (es→en). Our primary systems are built on deep Transformer with a large filter size. We also utilize R-Drop, data diversification, forward translation, back translation, data selection, finetuning and ensemble to improve the system performance. According to the official evaluation results in OCELoT or CodaLab, our unconstrained systems in en→de, de→en, en→fr, fr→en, en→zh and es→en (clinical terminology sub-track) get the highest BLEU scores among all submissions for the WMT22 biomedical translation task.

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HW-TSC Translation Systems for the WMT22 Chat Translation Task
Jinlong Yang | Zongyao Li | Daimeng Wei | Hengchao Shang | Xiaoyu Chen | Zhengzhe Yu | Zhiqiang Rao | Shaojun Li | Zhanglin Wu | Yuhao Xie | Yuanchang Luo | Ting Zhu | Yanqing Zhao | Lizhi Lei | Hao Yang | Ying Qin
Proceedings of the Seventh Conference on Machine Translation (WMT)

This paper describes the submissions of Huawei Translation Services Center (HW-TSC) to WMT22 chat translation shared task on English-Germany (en-de) bidirection with results of zore-shot and few-shot tracks. We use the deep transformer architecture with a lager parameter size. Our submissions to the WMT21 News Translation task are used as the baselines. We adopt strategies such as back translation, forward translation, domain transfer, data selection, and noisy forward translation in task, and achieve competitive results on the development set. We also test the effectiveness of document translation on chat tasks. Due to the lack of chat data, the results on the development set show that it is not as effective as sentence-level translation models.

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HW-TSC Systems for WMT22 Very Low Resource Supervised MT Task
Shaojun Li | Yuanchang Luo | Daimeng Wei | Zongyao Li | Hengchao Shang | Xiaoyu Chen | Zhanglin Wu | Jinlong Yang | Zhiqiang Rao | Zhengzhe Yu | Yuhao Xie | Lizhi Lei | Hao Yang | Ying Qin
Proceedings of the Seventh Conference on Machine Translation (WMT)

This paper describes the submissions of Huawei translation services center (HW-TSC) to the WMT22 Very Low Resource Supervised MT task. We participate in all 6 supervised tracks including all combinations between Upper/Lower Sorbian (Hsb/Dsb) and German (De). Our systems are build on deep Transformer with a large filter size. We use multilingual transfer with German-Czech (De-Cs) and German-Polish (De-Pl) parallel data. We also utilize regularized dropout (R-Drop), back translation, fine-tuning and ensemble to improve the system performance. According to the official evaluation results on OCELoT, our supervised systems of all 6 language directions get the highest BLEU scores among all submissions. Our pre-trained multilingual model for unsupervised De2Dsb and Dsb2De translation also gain highest BLEU.

2016

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LitWay, Discriminative Extraction for Different Bio-Events
Chen Li | Zhiqiang Rao | Xiangrong Zhang
Proceedings of the 4th BioNLP Shared Task Workshop