Wen Zhang


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The Xiaomi Text-to-Text Simultaneous Speech Translation System for IWSLT 2022
Bao Guo | Mengge Liu | Wen Zhang | Hexuan Chen | Chang Mu | Xiang Li | Jianwei Cui | Bin Wang | Yuhang Guo
Proceedings of the 19th International Conference on Spoken Language Translation (IWSLT 2022)

This system paper describes the Xiaomi Translation System for the IWSLT 2022 Simultaneous Speech Translation (noted as SST) shared task. We participate in the English-to-Mandarin Chinese Text-to-Text (noted as T2T) track. Our system is built based on the Transformer model with novel techniques borrowed from our recent research work. For the data filtering, language-model-based and rule-based methods are conducted to filter the data to obtain high-quality bilingual parallel corpora. We also strengthen our system with some dominating techniques related to data augmentation, such as knowledge distillation, tagged back-translation, and iterative back-translation. We also incorporate novel training techniques such as R-drop, deep model, and large batch training which have been shown to be beneficial to the naive Transformer model. In the SST scenario, several variations of extttwait-k strategies are explored. Furthermore, in terms of robustness, both data-based and model-based ways are used to reduce the sensitivity of our system to Automatic Speech Recognition (ASR) outputs. We finally design some inference algorithms and use the adaptive-ensemble method based on multiple model variants to further improve the performance of the system. Compared with strong baselines, fusing all techniques can improve our system by 2 extasciitilde3 BLEU scores under different latency regimes.


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Tencent Translation System for the WMT21 News Translation Task
Longyue Wang | Mu Li | Fangxu Liu | Shuming Shi | Zhaopeng Tu | Xing Wang | Shuangzhi Wu | Jiali Zeng | Wen Zhang
Proceedings of the Sixth Conference on Machine Translation

This paper describes Tencent Translation systems for the WMT21 shared task. We participate in the news translation task on three language pairs: Chinese-English, English-Chinese and German-English. Our systems are built on various Transformer models with novel techniques adapted from our recent research work. First, we combine different data augmentation methods including back-translation, forward-translation and right-to-left training to enlarge the training data. We also apply language coverage bias, data rejuvenation and uncertainty-based sampling approaches to select content-relevant and high-quality data from large parallel and monolingual corpora. Expect for in-domain fine-tuning, we also propose a fine-grained “one model one domain” approach to model characteristics of different news genres at fine-tuning and decoding stages. Besides, we use greed-based ensemble algorithm and transductive ensemble method to further boost our systems. Based on our success in the last WMT, we continuously employed advanced techniques such as large batch training, data selection and data filtering. Finally, our constrained Chinese-English system achieves 33.4 case-sensitive BLEU score, which is the highest among all submissions. The German-English system is ranked at second place accordingly.


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Logic-guided Semantic Representation Learning for Zero-Shot Relation Classification
Juan Li | Ruoxu Wang | Ningyu Zhang | Wen Zhang | Fan Yang | Huajun Chen
Proceedings of the 28th International Conference on Computational Linguistics

Relation classification aims to extract semantic relations between entity pairs from the sentences. However, most existing methods can only identify seen relation classes that occurred during training. To recognize unseen relations at test time, we explore the problem of zero-shot relation classification. Previous work regards the problem as reading comprehension or textual entailment, which have to rely on artificial descriptive information to improve the understandability of relation types. Thus, rich semantic knowledge of the relation labels is ignored. In this paper, we propose a novel logic-guided semantic representation learning model for zero-shot relation classification. Our approach builds connections between seen and unseen relations via implicit and explicit semantic representations with knowledge graph embeddings and logic rules. Extensive experimental results demonstrate that our method can generalize to unseen relation types and achieve promising improvements.


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Meta Relational Learning for Few-Shot Link Prediction in Knowledge Graphs
Mingyang Chen | Wen Zhang | Wei Zhang | Qiang Chen | Huajun Chen
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

Link prediction is an important way to complete knowledge graphs (KGs), while embedding-based methods, effective for link prediction in KGs, perform poorly on relations that only have a few associative triples. In this work, we propose a Meta Relational Learning (MetaR) framework to do the common but challenging few-shot link prediction in KGs, namely predicting new triples about a relation by only observing a few associative triples. We solve few-shot link prediction by focusing on transferring relation-specific meta information to make model learn the most important knowledge and learn faster, corresponding to relation meta and gradient meta respectively in MetaR. Empirically, our model achieves state-of-the-art results on few-shot link prediction KG benchmarks.

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Bridging the Gap between Training and Inference for Neural Machine Translation
Wen Zhang | Yang Feng | Fandong Meng | Di You | Qun Liu
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

Neural Machine Translation (NMT) generates target words sequentially in the way of predicting the next word conditioned on the context words. At training time, it predicts with the ground truth words as context while at inference it has to generate the entire sequence from scratch. This discrepancy of the fed context leads to error accumulation among the way. Furthermore, word-level training requires strict matching between the generated sequence and the ground truth sequence which leads to overcorrection over different but reasonable translations. In this paper, we address these issues by sampling context words not only from the ground truth sequence but also from the predicted sequence by the model during training, where the predicted sequence is selected with a sentence-level optimum. Experiment results on Chinese->English and WMT’14 English->German translation tasks demonstrate that our approach can achieve significant improvements on multiple datasets.


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Refining Source Representations with Relation Networks for Neural Machine Translation
Wen Zhang | Jiawei Hu | Yang Feng | Qun Liu
Proceedings of the 27th International Conference on Computational Linguistics

Although neural machine translation with the encoder-decoder framework has achieved great success recently, it still suffers drawbacks of forgetting distant information, which is an inherent disadvantage of recurrent neural network structure, and disregarding relationship between source words during encoding step. Whereas in practice, the former information and relationship are often useful in current step. We target on solving these problems and thus introduce relation networks to learn better representations of the source. The relation networks are able to facilitate memorization capability of recurrent neural network via associating source words with each other, this would also help retain their relationships. Then the source representations and all the relations are fed into the attention component together while decoding, with the main encoder-decoder framework unchanged. Experiments on several datasets show that our method can improve the translation performance significantly over the conventional encoder-decoder model and even outperform the approach involving supervised syntactic knowledge.

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Label-Free Distant Supervision for Relation Extraction via Knowledge Graph Embedding
Guanying Wang | Wen Zhang | Ruoxu Wang | Yalin Zhou | Xi Chen | Wei Zhang | Hai Zhu | Huajun Chen
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

Distant supervision is an effective method to generate large scale labeled data for relation extraction, which assumes that if a pair of entities appears in some relation of a Knowledge Graph (KG), all sentences containing those entities in a large unlabeled corpus are then labeled with that relation to train a relation classifier. However, when the pair of entities has multiple relationships in the KG, this assumption may produce noisy relation labels. This paper proposes a label-free distant supervision method, which makes no use of the relation labels under this inadequate assumption, but only uses the prior knowledge derived from the KG to supervise the learning of the classifier directly and softly. Specifically, we make use of the type information and the translation law derived from typical KG embedding model to learn embeddings for certain sentence patterns. As the supervision signal is only determined by the two aligned entities, neither hard relation labels nor extra noise-reduction model for the bag of sentences is needed in this way. The experiments show that the approach performs well in current distant supervision dataset.

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Speeding Up Neural Machine Translation Decoding by Cube Pruning
Wen Zhang | Liang Huang | Yang Feng | Lei Shen | Qun Liu
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

Although neural machine translation has achieved promising results, it suffers from slow translation speed. The direct consequence is that a trade-off has to be made between translation quality and speed, thus its performance can not come into full play. We apply cube pruning, a popular technique to speed up dynamic programming, into neural machine translation to speed up the translation. To construct the equivalence class, similar target hidden states are combined, leading to less RNN expansion operations on the target side and less softmax operations over the large target vocabulary. The experiments show that, at the same or even better translation quality, our method can translate faster compared with naive beam search by 3.3x on GPUs and 3.5x on CPUs.


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Automatic Cross-Lingual Similarization of Dependency Grammars for Tree-based Machine Translation
Wenbin Jiang | Wen Zhang | Jinan Xu | Rangjia Cai
Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing