Yuqi Zhang


2020

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Alibaba’s Submission for the WMT 2020 APE Shared Task: Improving Automatic Post-Editing with Pre-trained Conditional Cross-Lingual BERT
Jiayi Wang | Ke Wang | Kai Fan | Yuqi Zhang | Jun Lu | Xin Ge | Yangbin Shi | Yu Zhao
Proceedings of the Fifth Conference on Machine Translation

The goal of Automatic Post-Editing (APE) is basically to examine the automatic methods for correcting translation errors generated by an unknown machine translation (MT) system. This paper describes Alibaba’s submissions to the WMT 2020 APE Shared Task for the English-German language pair. We design a two-stage training pipeline. First, a BERT-like cross-lingual language model is pre-trained by randomly masking target sentences alone. Then, an additional neural decoder on the top of the pre-trained model is jointly fine-tuned for the APE task. We also apply an imitation learning strategy to augment a reasonable amount of pseudo APE training data, potentially preventing the model to overfit on the limited real training data and boosting the performance on held-out data. To verify our proposed model and data augmentation, we examine our approach with the well-known benchmarking English-German dataset from the WMT 2017 APE task. The experiment results demonstrate that our system significantly outperforms all other baselines and achieves the state-of-the-art performance. The final results on the WMT 2020 test dataset show that our submission can achieve +5.56 BLEU and -4.57 TER with respect to the official MT baseline.

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Alibaba Submission to the WMT20 Parallel Corpus Filtering Task
Jun Lu | Xin Ge | Yangbin Shi | Yuqi Zhang
Proceedings of the Fifth Conference on Machine Translation

This paper describes the Alibaba Machine Translation Group submissions to the WMT 2020 Shared Task on Parallel Corpus Filtering and Alignment. In the filtering task, three main methods are applied to evaluate the quality of the parallel corpus, i.e. a) Dual Bilingual GPT-2 model, b) Dual Conditional Cross-Entropy Model and c) IBM word alignment model. The scores of these models are combined by using a positive-unlabeled (PU) learning model and a brute-force search to obtain additional gains. Besides, a few simple but efficient rules are adopted to evaluate the quality and the diversity of the corpus. In the alignment-filtering task, the extraction pipeline of bilingual sentence pairs includes the following steps: bilingual lexicon mining, language identification, sentence segmentation and sentence alignment. The final result shows that, in both filtering and alignment tasks, our system significantly outperforms the LASER-based system.

2015

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The Karlsruhe Institute of Technology Translation Systems for the WMT 2015
Eunah Cho | Thanh-Le Ha | Jan Niehues | Teresa Herrmann | Mohammed Mediani | Yuqi Zhang | Alex Waibel
Proceedings of the Tenth Workshop on Statistical Machine Translation

2014

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The Karlsruhe Institute of Technology Translation Systems for the WMT 2014
Teresa Herrmann | Mohammed Mediani | Eunah Cho | Thanh-Le Ha | Jan Niehues | Isabel Slawik | Yuqi Zhang | Alex Waibel
Proceedings of the Ninth Workshop on Statistical Machine Translation

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The KIT translation systems for IWSLT 2014
Isabel Slawik | Mohammed Mediani | Jan Niehues | Yuqi Zhang | Eunah Cho | Teresa Herrmann | Thanh-Le Ha | Alex Waibel
Proceedings of the 11th International Workshop on Spoken Language Translation: Evaluation Campaign

In this paper, we present the KIT systems participating in the TED translation tasks of the IWSLT 2014 machine translation evaluation. We submitted phrase-based translation systems for all three official directions, namely English→German, German→English, and English→French, as well as for the optional directions English→Chinese and English→Arabic. For the official directions we built systems both for the machine translation as well as the spoken language translation track. This year we improved our systems’ performance over last year through n-best list rescoring using neural network-based translation and language models and novel preordering rules based on tree information of multiple syntactic levels. Furthermore, we could successfully apply a novel phrase extraction algorithm and transliteration of unknown words for Arabic. We also submitted a contrastive system for German→English built with stemmed German adjectives. For the SLT tracks, we used a monolingual translation system to translate the lowercased ASR hypotheses with all punctuation stripped to truecased, punctuated output as a preprocessing step to our usual translation system.

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Rule-based preordering on multiple syntactic levels in statistical machine translation
Ge Wu | Yuqi Zhang | Alexander Waibel
Proceedings of the 11th International Workshop on Spoken Language Translation: Papers

We propose a novel data-driven rule-based preordering approach, which uses the tree information of multiple syntactic levels. This approach extend the tree-based reordering from one level into multiple levels, which has the capability to process more complicated reordering cases. We have conducted experiments in English-to-Chinese and Chinese-to-English translation directions. Our results show that the approach has led to improved translation quality both when it was applied separately or when it was combined with some other reordering approaches. As our reordering approach was used alone, it showed an improvement of 1.61 in BLEU score in the English-to-Chinese translation direction and an improvement of 2.16 in BLEU score in the Chinese-to-English translation direction, in comparison with the baseline, which used no word reordering. As our preordering approach were combined with the short rule [1], long rule [2] and tree rule [3] based preordering approaches, it showed further improvements of up to 0.43 in BLEU score in the English-to-Chinese translation direction and further improvements of up to 0.3 in BLEU score in the Chinese-to-English translation direction. Through the translations that used our preordering approach, we have also found many translation examples with improved syntactic structures.

2013

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Measuring the Structural Importance through Rhetorical Structure Index
Narine Kokhlikyan | Alex Waibel | Yuqi Zhang | Joy Ying Zhang
Proceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

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The KIT translation systems for IWSLT 2013
Than-Le Ha | Teresa Herrmann | Jan Niehues | Mohammed Mediani | Eunah Cho | Yuqi Zhang | Isabel Slawik | Alex Waibel
Proceedings of the 10th International Workshop on Spoken Language Translation: Evaluation Campaign

In this paper, we present the KIT systems participating in all three official directions, namely English→German, German→English, and English→French, in translation tasks of the IWSLT 2013 machine translation evaluation. Additionally, we present the results for our submissions to the optional directions English→Chinese and English→Arabic. We used phrase-based translation systems to generate the translations. This year, we focused on adapting the systems towards ASR input. Furthermore, we investigated different reordering models as well as an extended discriminative word lexicon. Finally, we added a data selection approach for domain adaptation.

2012

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The KIT translation systems for IWSLT 2012
Mohammed Mediani | Yuqi Zhang | Thanh-Le Ha | Jan Niehues | Eunach Cho | Teresa Herrmann | Rainer Kärgel | Alexander Waibel
Proceedings of the 9th International Workshop on Spoken Language Translation: Evaluation Campaign

In this paper, we present the KIT systems participating in the English-French TED Translation tasks in the framework of the IWSLT 2012 machine translation evaluation. We also present several additional experiments on the English-German, English-Chinese and English-Arabic translation pairs. Our system is a phrase-based statistical machine translation system, extended with many additional models which were proven to enhance the translation quality. For instance, it uses the part-of-speech (POS)-based reordering, translation and language model adaptation, bilingual language model, word-cluster language model, discriminative word lexica (DWL), and continuous space language model. In addition to this, the system incorporates special steps in the preprocessing and in the post-processing step. In the preprocessing the noisy corpora are filtered by removing the noisy sentence pairs, whereas in the postprocessing the agreement between a noun and its surrounding words in the French translation is corrected based on POS tags with morphological information. Our system deals with speech transcription input by removing case information and punctuation except periods from the text translation model.

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The Karlsruhe Institute of Technology Translation Systems for the WMT 2012
Jan Niehues | Yuqi Zhang | Mohammed Mediani | Teresa Herrmann | Eunah Cho | Alex Waibel
Proceedings of the Seventh Workshop on Statistical Machine Translation

2009

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Are Unaligned Words Important for Machine Translation?
Yuqi Zhang | Evgeny Matusov | Hermann Ney
Proceedings of the 13th Annual conference of the European Association for Machine Translation

2008

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The RWTH machine translation system for IWSLT 2008.
David Vilar | Daniel Stein | Yuqi Zhang | Evgeny Matusov | Arne Mauser | Oliver Bender | Saab Mansour | Hermann Ney
Proceedings of the 5th International Workshop on Spoken Language Translation: Evaluation Campaign

RWTH’s system for the 2008 IWSLT evaluation consists of a combination of different phrase-based and hierarchical statistical machine translation systems. We participated in the translation tasks for the Chinese-to-English and Arabic-to-English language pairs. We investigated different preprocessing techniques, reordering methods for the phrase-based system, including reordering of speech lattices, and syntax-based enhancements for the hierarchical systems. We also tried the combination of the Arabic-to-English and Chinese-to-English outputs as an additional submission.

2007

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Chunk-Level Reordering of Source Language Sentences with Automatically Learned Rules for Statistical Machine Translation
Yuqi Zhang | Richard Zens | Hermann Ney
Proceedings of SSST, NAACL-HLT 2007 / AMTA Workshop on Syntax and Structure in Statistical Translation

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Improved chunk-level reordering for statistical machine translation
Yuqi Zhang | Richard Zens | Hermann Ney
Proceedings of the Fourth International Workshop on Spoken Language Translation

Inspired by previous chunk-level reordering approaches to statistical machine translation, this paper presents two methods to improve the reordering at the chunk level. By introducing a new lattice weighting factor and by reordering the training source data, an improvement is reported on TER and BLEU. Compared to the previous chunklevel reordering approach, the BLEU score improves 1.4% absolutely. The translation results are reported on IWSLT Chinese-English task.

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The RWTH machine translation system for IWSLT 2007
Arne Mauser | David Vilar | Gregor Leusch | Yuqi Zhang | Hermann Ney
Proceedings of the Fourth International Workshop on Spoken Language Translation

The RWTH system for the IWSLT 2007 evaluation is a combination of several statistical machine translation systems. The combination includes Phrase-Based models, a n-gram translation model and a hierarchical phrase model. We describe the individual systems and the method that was used for combining the system outputs. Compared to our 2006 system, we newly introduce a hierarchical phrase-based translation model and show improvements in system combination for Machine Translation. RWTH participated in the Italian-to-English and Chinese-to-English translation directions.

2005

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The RWTH Phrase-based Statistical Machine Translation System
Richard Zens | Oliver Bender | Sasa Hasan | Shahram Khadivi | Evgeny Matusov | Jia Xu | Yuqi Zhang | Hermann Ney
Proceedings of the Second International Workshop on Spoken Language Translation

2002

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Chinese Base-Phrases Chunking
Yuqi Zhang | Qiang Zhou
COLING-02: The First SIGHAN Workshop on Chinese Language Processing