Jun Xie


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A Reinforced Generation of Adversarial Examples for Neural Machine Translation
Wei Zou | Shujian Huang | Jun Xie | Xinyu Dai | Jiajun Chen
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

Neural machine translation systems tend to fail on less decent inputs despite its significant efficacy, which may significantly harm the credibility of these systems—fathoming how and when neural-based systems fail in such cases is critical for industrial maintenance. Instead of collecting and analyzing bad cases using limited handcrafted error features, here we investigate this issue by generating adversarial examples via a new paradigm based on reinforcement learning. Our paradigm could expose pitfalls for a given performance metric, e.g., BLEU, and could target any given neural machine translation architecture. We conduct experiments of adversarial attacks on two mainstream neural machine translation architectures, RNN-search, and Transformer. The results show that our method efficiently produces stable attacks with meaning-preserving adversarial examples. We also present a qualitative and quantitative analysis for the preference pattern of the attack, demonstrating its capability of pitfall exposure.

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Improving Event Detection via Open-domain Trigger Knowledge
Meihan Tong | Bin Xu | Shuai Wang | Yixin Cao | Lei Hou | Juanzi Li | Jun Xie
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

Event Detection (ED) is a fundamental task in automatically structuring texts. Due to the small scale of training data, previous methods perform poorly on unseen/sparsely labeled trigger words and are prone to overfitting densely labeled trigger words. To address the issue, we propose a novel Enrichment Knowledge Distillation (EKD) model to leverage external open-domain trigger knowledge to reduce the in-built biases to frequent trigger words in annotations. Experiments on benchmark ACE2005 show that our model outperforms nine strong baselines, is especially effective for unseen/sparsely labeled trigger words. The source code is released on https://github.com/shuaiwa16/ekd.git.

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Making the Best Use of Review Summary for Sentiment Analysis
Sen Yang | Leyang Cui | Jun Xie | Yue Zhang
Proceedings of the 28th International Conference on Computational Linguistics

Sentiment analysis provides a useful overview of customer review contents. Many review websites allow a user to enter a summary in addition to a full review. Intuitively, summary information may give additional benefit for review sentiment analysis. In this paper, we conduct a study to exploit methods for better use of summary information. We start by finding out that the sentimental signal distribution of a review and that of its corresponding summary are in fact complementary to each other. We thus explore various architectures to better guide the interactions between the two and propose a hierarchically-refined review-centric attention model. Empirical results show that our review-centric model can make better use of user-written summaries for review sentiment analysis, and is also more effective compared to existing methods when the user summary is replaced with summary generated by an automatic summarization system.

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Emotion Classification by Jointly Learning to Lexiconize and Classify
Deyu Zhou | Shuangzhi Wu | Qing Wang | Jun Xie | Zhaopeng Tu | Mu Li
Proceedings of the 28th International Conference on Computational Linguistics

Emotion lexicons have been shown effective for emotion classification (Baziotis et al., 2018). Previous studies handle emotion lexicon construction and emotion classification separately. In this paper, we propose an emotional network (EmNet) to jointly learn sentence emotions and construct emotion lexicons which are dynamically adapted to a given context. The dynamic emotion lexicons are useful for handling words with multiple emotions based on different context, which can effectively improve the classification accuracy. We validate the approach on two representative architectures – LSTM and BERT, demonstrating its superiority on identifying emotions in Tweets. Our model outperforms several approaches proposed in previous studies and achieves new state-of-the-art on the benchmark Twitter dataset.

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What Have We Achieved on Text Summarization?
Dandan Huang | Leyang Cui | Sen Yang | Guangsheng Bao | Kun Wang | Jun Xie | Yue Zhang
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Deep learning has led to significant improvement in text summarization with various methods investigated and improved ROUGE scores reported over the years. However, gaps still exist between summaries produced by automatic summarizers and human professionals. Aiming to gain more understanding of summarization systems with respect to their strengths and limits on a fine-grained syntactic and semantic level, we consult the Multidimensional Quality Metric (MQM) and quantify 8 major sources of errors on 10 representative summarization models manually. Primarily, we find that 1) under similar settings, extractive summarizers are in general better than their abstractive counterparts thanks to strength in faithfulness and factual-consistency; 2) milestone techniques such as copy, coverage and hybrid extractive/abstractive methods do bring specific improvements but also demonstrate limitations; 3) pre-training techniques, and in particular sequence-to-sequence pre-training, are highly effective for improving text summarization, with BART giving the best results.


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Specificity-Driven Cascading Approach for Unsupervised Sentiment Modification
Pengcheng Yang | Junyang Lin | Jingjing Xu | Jun Xie | Qi Su | Xu Sun
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

The task of unsupervised sentiment modification aims to reverse the sentiment polarity of the input text while preserving its semantic content without any parallel data. Most previous work follows a two-step process. They first separate the content from the original sentiment, and then directly generate text with the target sentiment only based on the content produced by the first step. However, the second step bears both the target sentiment addition and content reconstruction, thus resulting in a lack of specific information like proper nouns in the generated text. To remedy this, we propose a specificity-driven cascading approach in this work, which can effectively increase the specificity of the generated text and further improve content preservation. In addition, we propose a more reasonable metric to evaluate sentiment modification. The experiments show that our approach outperforms competitive baselines by a large margin, which achieves 11% and 38% relative improvements of the overall metric on the Yelp and Amazon datasets, respectively.


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Multi-Domain Neural Machine Translation with Word-Level Domain Context Discrimination
Jiali Zeng | Jinsong Su | Huating Wen | Yang Liu | Jun Xie | Yongjing Yin | Jianqiang Zhao
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

With great practical value, the study of Multi-domain Neural Machine Translation (NMT) mainly focuses on using mixed-domain parallel sentences to construct a unified model that allows translation to switch between different domains. Intuitively, words in a sentence are related to its domain to varying degrees, so that they will exert disparate impacts on the multi-domain NMT modeling. Based on this intuition, in this paper, we devote to distinguishing and exploiting word-level domain contexts for multi-domain NMT. To this end, we jointly model NMT with monolingual attention-based domain classification tasks and improve NMT as follows: 1) Based on the sentence representations produced by a domain classifier and an adversarial domain classifier, we generate two gating vectors and use them to construct domain-specific and domain-shared annotations, for later translation predictions via different attention models; 2) We utilize the attention weights derived from target-side domain classifier to adjust the weights of target words in the training objective, enabling domain-related words to have greater impacts during model training. Experimental results on Chinese-English and English-French multi-domain translation tasks demonstrate the effectiveness of the proposed model. Source codes of this paper are available on Github https://github.com/DeepLearnXMU/WDCNMT.

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Neural Machine Translation with Decoding History Enhanced Attention
Mingxuan Wang | Jun Xie | Zhixing Tan | Jinsong Su | Deyi Xiong | Chao Bian
Proceedings of the 27th International Conference on Computational Linguistics

Neural machine translation with source-side attention have achieved remarkable performance. however, there has been little work exploring to attend to the target-side which can potentially enhance the memory capbility of NMT. We reformulate a Decoding History Enhanced Attention mechanism (DHEA) to render NMT model better at selecting both source-side and target-side information. DHA enables dynamic control of the ratios at which source and target contexts contribute to the generation of target words, offering a way to weakly induce structure relations among both source and target tokens. It also allows training errors to be directly back-propagated through short-cut connections and effectively alleviates the gradient vanishing problem. The empirical study on Chinese-English translation shows that our model with proper configuration can improve by 0:9 BLEU upon Transformer and the best reported results in the dataset. On WMT14 English-German task and a larger WMT14 English-French task, our model achieves comparable results with the state-of-the-art.

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Tencent Neural Machine Translation Systems for WMT18
Mingxuan Wang | Li Gong | Wenhuan Zhu | Jun Xie | Chao Bian
Proceedings of the Third Conference on Machine Translation: Shared Task Papers

We participated in the WMT 2018 shared news translation task on English↔Chinese language pair. Our systems are based on attentional sequence-to-sequence models with some form of recursion and self-attention. Some data augmentation methods are also introduced to improve the translation performance. The best translation result is obtained with ensemble and reranking techniques. Our Chinese→English system achieved the highest cased BLEU score among all 16 submitted systems, and our English→Chinese system ranked the third out of 18 submitted systems.


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The DCU-ICTCAS MT system at WMT 2014 on German-English Translation Task
Liangyou Li | Xiaofeng Wu | Santiago Cortés Vaíllo | Jun Xie | Andy Way | Qun Liu
Proceedings of the Ninth Workshop on Statistical Machine Translation

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Transformation and Decomposition for Efficiently Implementing and Improving Dependency-to-String Model In Moses
Liangyou Li | Jun Xie | Andy Way | Qun Liu
Proceedings of SSST-8, Eighth Workshop on Syntax, Semantics and Structure in Statistical Translation

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A Dependency Edge-based Transfer Model for Statistical Machine Translation
Hongshen Chen | Jun Xie | Fandong Meng | Wenbin Jiang | Qun Liu
Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: Technical Papers

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RED: A Reference Dependency Based MT Evaluation Metric
Hui Yu | Xiaofeng Wu | Jun Xie | Wenbin Jiang | Qun Liu | Shouxun Lin
Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: Technical Papers

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Augment Dependency-to-String Translation with Fixed and Floating Structures
Jun Xie | Jinan Xu | Qun Liu
Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: Technical Papers


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Translation with Source Constituency and Dependency Trees
Fandong Meng | Jun Xie | Linfeng Song | Yajuan Lü | Qun Liu
Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing

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The CNGL-DCU-Prompsit Translation Systems for WMT13
Raphael Rubino | Antonio Toral | Santiago Cortés Vaíllo | Jun Xie | Xiaofeng Wu | Stephen Doherty | Qun Liu
Proceedings of the Eighth Workshop on Statistical Machine Translation


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A novel dependency-to-string model for statistical machine translation
Jun Xie | Haitao Mi | Qun Liu
Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing