Zhongjun He


2023

pdf
Improving Zero-shot Multilingual Neural Machine Translation by Leveraging Cross-lingual Consistency Regularization
Pengzhi Gao | Liwen Zhang | Zhongjun He | Hua Wu | Haifeng Wang
Findings of the Association for Computational Linguistics: ACL 2023

The multilingual neural machine translation (NMT) model has a promising capability of zero-shot translation, where it could directly translate between language pairs unseen during training. For good transfer performance from supervised directions to zero-shot directions, the multilingual NMT model is expected to learn universal representations across different languages. This paper introduces a cross-lingual consistency regularization, CrossConST, to bridge the representation gap among different languages and boost zero-shot translation performance. The theoretical analysis shows that CrossConST implicitly maximizes the probability distribution for zero-shot translation, and the experimental results on both low-resource and high-resource benchmarks show that CrossConST consistently improves the translation performance. The experimental analysis also proves that CrossConST could close the sentence representation gap and better align the representation space. Given the universality and simplicity of CrossConST, we believe it can serve as a strong baseline for future multilingual NMT research.

2022

pdf
Bi-SimCut: A Simple Strategy for Boosting Neural Machine Translation
Pengzhi Gao | Zhongjun He | Hua Wu | Haifeng Wang
Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

We introduce Bi-SimCut: a simple but effective training strategy to boost neural machine translation (NMT) performance. It consists of two procedures: bidirectional pretraining and unidirectional finetuning. Both procedures utilize SimCut, a simple regularization method that forces the consistency between the output distributions of the original and the cutoff sentence pairs. Without leveraging extra dataset via back-translation or integrating large-scale pretrained model, Bi-SimCut achieves strong translation performance across five translation benchmarks (data sizes range from 160K to 20.2M): BLEU scores of 31.16 for ende and 38.37 for deen on the IWSLT14 dataset, 30.78 for ende and 35.15 for deen on the WMT14 dataset, and 27.17 for zhen on the WMT17 dataset. SimCut is not a new method, but a version of Cutoff (Shen et al., 2020) simplified and adapted for NMT, and it could be considered as a perturbation-based method. Given the universality and simplicity of Bi-SimCut and SimCut, we believe they can serve as strong baselines for future NMT research.

pdf
Non-Autoregressive Chinese ASR Error Correction with Phonological Training
Zheng Fang | Ruiqing Zhang | Zhongjun He | Hua Wu | Yanan Cao
Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

Automatic Speech Recognition (ASR) is an efficient and widely used input method that transcribes speech signals into text. As the errors introduced by ASR systems will impair the performance of downstream tasks, we introduce a post-processing error correction method, PhVEC, to correct errors in text space. For the errors in ASR result, existing works mainly focus on fixed-length corrections, modifying each wrong token to a correct one (one-to-one correction), but rarely consider the variable-length correction (one-to-many or many-to-one correction). In this paper, we propose an efficient non-autoregressive (NAR) method for Chinese ASR error correction for both cases. Instead of conventionally predicting the sentence length in NAR methods, we propose a novel approach that uses phonological tokens to extend the source sentence for variable-length correction, enabling our model to generate phonetically similar corrections. Experimental results on datasets of different domains show that our method achieves significant improvement in word error rate reduction and speeds up the inference by 6.2 times compared with the autoregressive model.

pdf bib
Findings of the Third Workshop on Automatic Simultaneous Translation
Ruiqing Zhang | Chuanqiang Zhang | Zhongjun He | Hua Wu | Haifeng Wang | Liang Huang | Qun Liu | Julia Ive | Wolfgang Macherey
Proceedings of the Third Workshop on Automatic Simultaneous Translation

This paper reports the results of the shared task we hosted on the Third Workshop of Automatic Simultaneous Translation (AutoSimTrans). The shared task aims to promote the development of text-to-text and speech-to-text simultaneous translation, and includes Chinese-English and English-Spanish tracks. The number of systems submitted this year has increased fourfold compared with last year. Additionally, the top 1 ranked system in the speech-to-text track is the first end-to-end submission we have received in the past three years, which has shown great potential. This paper reports the results and descriptions of the 14 participating teams, compares different evaluation metrics, and revisits the ranking method.

pdf
Learning Adaptive Segmentation Policy for End-to-End Simultaneous Translation
Ruiqing Zhang | Zhongjun He | Hua Wu | Haifeng Wang
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

End-to-end simultaneous speech-to-text translation aims to directly perform translation from streaming source speech to target text with high translation quality and low latency. A typical simultaneous translation (ST) system consists of a speech translation model and a policy module, which determines when to wait and when to translate. Thus the policy is crucial to balance translation quality and latency. Conventional methods usually adopt fixed policies, e.g. segmenting the source speech with a fixed length and generating translation. However, this method ignores contextual information and suffers from low translation quality. This paper proposes an adaptive segmentation policy for end-to-end ST. Inspired by human interpreters, the policy learns to segment the source streaming speech into meaningful units by considering both acoustic features and translation history, maintaining consistency between the segmentation and translation. Experimental results on English-German and Chinese-English show that our method achieves a good accuracy-latency trade-off over recently proposed state-of-the-art methods.

2021

pdf
Correcting Chinese Spelling Errors with Phonetic Pre-training
Ruiqing Zhang | Chao Pang | Chuanqiang Zhang | Shuohuan Wang | Zhongjun He | Yu Sun | Hua Wu | Haifeng Wang
Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021

pdf
Mixup Decoding for Diverse Machine Translation
Jicheng Li | Pengzhi Gao | Xuanfu Wu | Yang Feng | Zhongjun He | Hua Wu | Haifeng Wang
Findings of the Association for Computational Linguistics: EMNLP 2021

Diverse machine translation aims at generating various target language translations for a given source language sentence. To leverage the linear relationship in the sentence latent space introduced by the mixup training, we propose a novel method, MixDiversity, to generate different translations for the input sentence by linearly interpolating it with different sentence pairs sampled from the training corpus during decoding. To further improve the faithfulness and diversity of the translations, we propose two simple but effective approaches to select diverse sentence pairs in the training corpus and adjust the interpolation weight for each pair correspondingly. Moreover, by controlling the interpolation weight, our method can achieve the trade-off between faithfulness and diversity without any additional training, which is required in most of the previous methods. Experiments on WMT’16 en-ro, WMT’14 en-de, and WMT’17 zh-en are conducted to show that our method substantially outperforms all previous diverse machine translation methods.

pdf bib
Proceedings of the Second Workshop on Automatic Simultaneous Translation
Hua Wu | Colin Cherry | Liang Huang | Zhongjun He | Qun Liu | Maha Elbayad | Mark Liberman | Haifeng Wang | Mingbo Ma | Ruiqing Zhang
Proceedings of the Second Workshop on Automatic Simultaneous Translation

pdf
BSTC: A Large-Scale Chinese-English Speech Translation Dataset
Ruiqing Zhang | Xiyang Wang | Chuanqiang Zhang | Zhongjun He | Hua Wu | Zhi Li | Haifeng Wang | Ying Chen | Qinfei Li
Proceedings of the Second Workshop on Automatic Simultaneous Translation

This paper presents BSTC (Baidu Speech Translation Corpus), a large-scale Chinese-English speech translation dataset. This dataset is constructed based on a collection of licensed videos of talks or lectures, including about 68 hours of Mandarin data, their manual transcripts and translations into English, as well as automated transcripts by an automatic speech recognition (ASR) model. We have further asked three experienced interpreters to simultaneously interpret the testing talks in a mock conference setting. This corpus is expected to promote the research of automatic simultaneous translation as well as the development of practical systems. We have organized simultaneous translation tasks and used this corpus to evaluate automatic simultaneous translation systems.

pdf
Findings of the Second Workshop on Automatic Simultaneous Translation
Ruiqing Zhang | Chuanqiang Zhang | Zhongjun He | Hua Wu | Haifeng Wang
Proceedings of the Second Workshop on Automatic Simultaneous Translation

This paper presents the results of the shared task of the 2nd Workshop on Automatic Simultaneous Translation (AutoSimTrans). The task includes two tracks, one for text-to-text translation and one for speech-to-text, requiring participants to build systems to translate from either the source text or speech into the target text. Different from traditional machine translation, the AutoSimTrans shared task evaluates not only translation quality but also latency. We propose a metric “Monotonic Optimal Sequence” (MOS) considering both quality and latency to rank the submissions. We also discuss some important open issues in simultaneous translation.

2020

pdf bib
Proceedings of the First Workshop on Automatic Simultaneous Translation
Hua Wu | Colin Cherry | Liang Huang | Zhongjun He | Mark Liberman | James Cross | Yang Liu
Proceedings of the First Workshop on Automatic Simultaneous Translation

pdf
Learning Adaptive Segmentation Policy for Simultaneous Translation
Ruiqing Zhang | Chuanqiang Zhang | Zhongjun He | Hua Wu | Haifeng Wang
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Balancing accuracy and latency is a great challenge for simultaneous translation. To achieve high accuracy, the model usually needs to wait for more streaming text before translation, which results in increased latency. However, keeping low latency would probably hurt accuracy. Therefore, it is essential to segment the ASR output into appropriate units for translation. Inspired by human interpreters, we propose a novel adaptive segmentation policy for simultaneous translation. The policy learns to segment the source text by considering possible translations produced by the translation model, maintaining consistency between the segmentation and translation. Experimental results on Chinese-English and German-English translation show that our method achieves a better accuracy-latency trade-off over recently proposed state-of-the-art methods.

pdf
Simultaneous Translation
Liang Huang | Colin Cherry | Mingbo Ma | Naveen Arivazhagan | Zhongjun He
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: Tutorial Abstracts

Simultaneous translation, which performs translation concurrently with the source speech, is widely useful in many scenarios such as international conferences, negotiations, press releases, legal proceedings, and medicine. This problem has long been considered one of the hardest problems in AI and one of its holy grails. Recently, with rapid improvements in machine translation, speech recognition, and speech synthesis, there has been exciting progress towards simultaneous translation. This tutorial will focus on the design and evaluation of policies for simultaneous translation, to leave attendees with a deep technical understanding of the history, the recent advances, and the remaining challenges in this field.

2019

pdf
Multi-agent Learning for Neural Machine Translation
Tianchi Bi | Hao Xiong | Zhongjun He | Hua Wu | Haifeng Wang
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

Conventional Neural Machine Translation (NMT) models benefit from the training with an additional agent, e.g., dual learning, and bidirectional decoding with one agent decod- ing from left to right and the other decoding in the opposite direction. In this paper, we extend the training framework to the multi-agent sce- nario by introducing diverse agents in an in- teractive updating process. At training time, each agent learns advanced knowledge from others, and they work together to improve translation quality. Experimental results on NIST Chinese-English, IWSLT 2014 German- English, WMT 2014 English-German and large-scale Chinese-English translation tasks indicate that our approach achieves absolute improvements over the strong baseline sys- tems and shows competitive performance on all tasks.

pdf
STACL: Simultaneous Translation with Implicit Anticipation and Controllable Latency using Prefix-to-Prefix Framework
Mingbo Ma | Liang Huang | Hao Xiong | Renjie Zheng | Kaibo Liu | Baigong Zheng | Chuanqiang Zhang | Zhongjun He | Hairong Liu | Xing Li | Hua Wu | Haifeng Wang
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

Simultaneous translation, which translates sentences before they are finished, is use- ful in many scenarios but is notoriously dif- ficult due to word-order differences. While the conventional seq-to-seq framework is only suitable for full-sentence translation, we pro- pose a novel prefix-to-prefix framework for si- multaneous translation that implicitly learns to anticipate in a single translation model. Within this framework, we present a very sim- ple yet surprisingly effective “wait-k” policy trained to generate the target sentence concur- rently with the source sentence, but always k words behind. Experiments show our strat- egy achieves low latency and reasonable qual- ity (compared to full-sentence translation) on 4 directions: zh↔en and de↔en.

pdf
Robust Neural Machine Translation with Joint Textual and Phonetic Embedding
Hairong Liu | Mingbo Ma | Liang Huang | Hao Xiong | Zhongjun He
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

Neural machine translation (NMT) is notoriously sensitive to noises, but noises are almost inevitable in practice. One special kind of noise is the homophone noise, where words are replaced by other words with similar pronunciations. We propose to improve the robustness of NMT to homophone noises by 1) jointly embedding both textual and phonetic information of source sentences, and 2) augmenting the training dataset with homophone noises. Interestingly, to achieve better translation quality and more robustness, we found that most (though not all) weights should be put on the phonetic rather than textual information. Experiments show that our method not only significantly improves the robustness of NMT to homophone noises, but also surprisingly improves the translation quality on some clean test sets.

pdf
Baidu Neural Machine Translation Systems for WMT19
Meng Sun | Bojian Jiang | Hao Xiong | Zhongjun He | Hua Wu | Haifeng Wang
Proceedings of the Fourth Conference on Machine Translation (Volume 2: Shared Task Papers, Day 1)

In this paper we introduce the systems Baidu submitted for the WMT19 shared task on Chinese<->English news translation. Our systems are based on the Transformer architecture with some effective improvements. Data selection, back translation, data augmentation, knowledge distillation, domain adaptation, model ensemble and re-ranking are employed and proven effective in our experiments. Our Chinese->English system achieved the highest case-sensitive BLEU score among all constrained submissions, and our English->Chinese system ranked the second in all submissions.

2018

pdf
Addressing Troublesome Words in Neural Machine Translation
Yang Zhao | Jiajun Zhang | Zhongjun He | Chengqing Zong | Hua Wu
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

One of the weaknesses of Neural Machine Translation (NMT) is in handling lowfrequency and ambiguous words, which we refer as troublesome words. To address this problem, we propose a novel memoryenhanced NMT method. First, we investigate different strategies to define and detect the troublesome words. Then, a contextual memory is constructed to memorize which target words should be produced in what situations. Finally, we design a hybrid model to dynamically access the contextual memory so as to correctly translate the troublesome words. The extensive experiments on Chinese-to-English and English-to-German translation tasks demonstrate that our method significantly outperforms the strong baseline models in translation quality, especially in handling troublesome words.

2016

pdf
Minimum Risk Training for Neural Machine Translation
Shiqi Shen | Yong Cheng | Zhongjun He | Wei He | Hua Wu | Maosong Sun | Yang Liu
Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

pdf
Semi-Supervised Learning for Neural Machine Translation
Yong Cheng | Wei Xu | Zhongjun He | Wei He | Hua Wu | Maosong Sun | Yang Liu
Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

2015

pdf
Baidu Translate: Research and Products
Zhongjun He
Proceedings of the Fourth Workshop on Hybrid Approaches to Translation (HyTra)

2014

pdf
Transformation from Discontinuous to Continuous Word Alignment Improves Translation Quality
Zhongjun He | Hua Wu | Haifeng Wang | Ting Liu
Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP)

pdf
Improving Pivot-Based Statistical Machine Translation by Pivoting the Co-occurrence Count of Phrase Pairs
Xiaoning Zhu | Zhongjun He | Hua Wu | Conghui Zhu | Haifeng Wang | Tiejun Zhao
Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP)

2013

pdf
Improving Pivot-Based Statistical Machine Translation Using Random Walk
Xiaoning Zhu | Zhongjun He | Hua Wu | Haifeng Wang | Conghui Zhu | Tiejun Zhao
Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing

2010

pdf
Maximum Entropy Based Phrase Reordering for Hierarchical Phrase-Based Translation
Zhongjun He | Yao Meng | Hao Yu
Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing

pdf
Learning Phrase Boundaries for Hierarchical Phrase-based Translation
Zhongjun He | Yao Meng | Hao Yu
Coling 2010: Posters

pdf
Extending the Hierarchical Phrase Based Model with Maximum Entropy Based BTG
Zhongjun He | Yao Meng | Hao Yu
Proceedings of the 9th Conference of the Association for Machine Translation in the Americas: Research Papers

In the hierarchical phrase based (HPB) translation model, in addition to hierarchical phrase pairs extracted from bi-text, glue rules are used to perform serial combination of phrases. However, this basic method for combining phrases is not sufficient for phrase reordering. In this paper, we extend the HPB model with maximum entropy based bracketing transduction grammar (BTG), which provides content-dependent combination of neighboring phrases in two ways: serial or inverse. Experimental results show that the extended HPB system achieves absolute improvements of 0.9∼1.8 BLEU points over the baseline for large-scale translation tasks.

2009

pdf
Reducing SMT Rule Table with Monolingual Key Phrase
Zhongjun He | Yao Meng | Yajuan Lü | Hao Yu | Qun Liu
Proceedings of the ACL-IJCNLP 2009 Conference Short Papers

2008

pdf
Improving Statistical Machine Translation using Lexicalized Rule Selection
Zhongjun He | Qun Liu | Shouxun Lin
Proceedings of the 22nd International Conference on Computational Linguistics (Coling 2008)

pdf
Partial Matching Strategy for Phrase-based Statistical Machine Translation
Zhongjun He | Qun Liu | Shouxun Lin
Proceedings of ACL-08: HLT, Short Papers

pdf
The ICT system description for IWSLT 2008.
Yang Liu | Zhongjun He | Haitao Mi | Yun Huang | Yang Feng | Wenbin Jiang | Yajuan Lu | Qun Liu
Proceedings of the 5th International Workshop on Spoken Language Translation: Evaluation Campaign

This paper presents a description for the ICT systems involved in the IWSLT 2008 evaluation campaign. This year, we participated in Chinese-English and English-Chinese translation directions. Four statistical machine translation systems were used: one linguistically syntax-based, two formally syntax-based, and one phrase-based. The outputs of the four SMT systems were fed to a sentence-level system combiner, which was expected to produce better translations than single systems. We will report the results of the four single systems and the combiner on both the development and test sets.

pdf
Maximum Entropy based Rule Selection Model for Syntax-based Statistical Machine Translation
Qun Liu | Zhongjun He | Yang Liu | Shouxun Lin
Proceedings of the 2008 Conference on Empirical Methods in Natural Language Processing

2007

pdf
The ICT statistical machine translation systems for IWSLT 2007
Zhongjun He | Haitao Mi | Yang Liu | Deyi Xiong | Weihua Luo | Yun Huang | Zhixiang Ren | Yajuan Lu | Qun Liu
Proceedings of the Fourth International Workshop on Spoken Language Translation

In this paper, we give an overview of the ICT statistical machine translation systems for the evaluation campaign of the International Workshop on Spoken Language Translation (IWSLT) 2007. In this year’s evaluation, we participated in the Chinese-English transcript translation task, and developed three systems based on different techniques: a formally syntax-based system Bruin, an extended phrase-based system Confucius and a linguistically syntax-based system Lynx. We will describe the models of these three systems, and compare their performance in detail. We set Bruin as our primary system, which ranks 2 among the 15 primary results according to the official evaluation results.