Yang Liu

刘洋; ICT, Tsinghua, Beijing Academy of Artificial Intelligence

Other people with similar names: Yang Liu (May refer to several people), Yang Liu (Edinburgh), Yang (Janet) Liu (刘洋; Georgetown), Yang Liu (Georgetown University), Yang Liu (刘扬; Ph.D Purdue; ICSI, Dallas, Facebook, Liulishuo, Amazon), Yang Liu (Univ. of Michigan, UC Santa Cruz)


2021

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Proceedings of the 20th Chinese National Conference on Computational Linguistics
Sheng Li (李生) | Maosong Sun (孙茂松) | Yang Liu (刘洋) | Hua Wu (吴华) | Kang Liu (刘康) | Wanxiang Che (车万翔) | Shizhu He (何世柱) | Gaoqi Rao (饶高琦)
Proceedings of the 20th Chinese National Conference on Computational Linguistics

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中美学者学术英语写作中词汇难度特征比较研究——以计算语言学领域论文为例(A Comparative Study of the Features of Lexical Sophistication in Academic English Writing by Chinese and American)
Yonghui Xie (谢永慧) | Yang Liu (刘洋) | Erhong Yang (杨尔弘) | Liner Yang (杨麟儿)
Proceedings of the 20th Chinese National Conference on Computational Linguistics

“学术英语写作在国际学术交流中的作用日益凸显,然而对于英语非母语者,学术英语写作是困难的,为此本文对计算语言领域中美学者学术英语写作中词汇难度特征做比较研究。自构建1132篇中美论文全文语料库,统计语料中484个词汇难度特征值。经过特征筛选与因子分析的降维处理得到表现较好的五个维度。最后计算中美学者论文的维度分从而比较差异,发现美国学者的论文相较中国学者的论文中词汇单位更具常用性、二元词串更具稳固性、三元词串更具稳固性、虚词更具复杂性、词类更具关联性。主要原因在于统计特征值时借助的外部资源库与美国学者的论文更贴近,且中国学者没有完全掌握该领域学术写作的习惯。因此,中国学者可充分利用英语本族语者构建的资源库,从而产出更为地道与流利的学术英语论文。”

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Mask-Align: Self-Supervised Neural Word Alignment
Chi Chen | Maosong Sun | Yang Liu
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

Word alignment, which aims to align translationally equivalent words between source and target sentences, plays an important role in many natural language processing tasks. Current unsupervised neural alignment methods focus on inducing alignments from neural machine translation models, which does not leverage the full context in the target sequence. In this paper, we propose Mask-Align, a self-supervised word alignment model that takes advantage of the full context on the target side. Our model masks out each target token and predicts it conditioned on both source and the remaining target tokens. This two-step process is based on the assumption that the source token contributing most to recovering the masked target token should be aligned. We also introduce an attention variant called leaky attention, which alleviates the problem of unexpected high cross-attention weights on special tokens such as periods. Experiments on four language pairs show that our model outperforms previous unsupervised neural aligners and obtains new state-of-the-art results.

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Transfer Learning for Sequence Generation: from Single-source to Multi-source
Xuancheng Huang | Jingfang Xu | Maosong Sun | Yang Liu
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

Multi-source sequence generation (MSG) is an important kind of sequence generation tasks that takes multiple sources, including automatic post-editing, multi-source translation, multi-document summarization, etc. As MSG tasks suffer from the data scarcity problem and recent pretrained models have been proven to be effective for low-resource downstream tasks, transferring pretrained sequence-to-sequence models to MSG tasks is essential. Although directly finetuning pretrained models on MSG tasks and concatenating multiple sources into a single long sequence is regarded as a simple method to transfer pretrained models to MSG tasks, we conjecture that the direct finetuning method leads to catastrophic forgetting and solely relying on pretrained self-attention layers to capture cross-source information is not sufficient. Therefore, we propose a two-stage finetuning method to alleviate the pretrain-finetune discrepancy and introduce a novel MSG model with a fine encoder to learn better representations in MSG tasks. Experiments show that our approach achieves new state-of-the-art results on the WMT17 APE task and multi-source translation task using the WMT14 test set. When adapted to document-level translation, our framework outperforms strong baselines significantly.

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Alternated Training with Synthetic and Authentic Data for Neural Machine Translation
Rui Jiao | Zonghan Yang | Maosong Sun | Yang Liu
Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021

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On the Language Coverage Bias for Neural Machine Translation
Shuo Wang | Zhaopeng Tu | Zhixing Tan | Shuming Shi | Maosong Sun | Yang Liu
Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021

2020

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

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Accurate Word Alignment Induction from Neural Machine Translation
Yun Chen | Yang Liu | Guanhua Chen | Xin Jiang | Qun Liu
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Despite its original goal to jointly learn to align and translate, prior researches suggest that Transformer captures poor word alignments through its attention mechanism. In this paper, we show that attention weights do capture accurate word alignments and propose two novel word alignment induction methods Shift-Att and Shift-AET. The main idea is to induce alignments at the step when the to-be-aligned target token is the decoder input rather than the decoder output as in previous work. Shift-Att is an interpretation method that induces alignments from the attention weights of Transformer and does not require parameter update or architecture change. Shift-AET extracts alignments from an additional alignment module which is tightly integrated into Transformer and trained in isolation with supervision from symmetrized Shift-Att alignments. Experiments on three publicly available datasets demonstrate that both methods perform better than their corresponding neural baselines and Shift-AET significantly outperforms GIZA++ by 1.4-4.8 AER points.

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Proceedings of the 19th Chinese National Conference on Computational Linguistics
Maosong Sun (孙茂松) | Sujian Li (李素建) | Yue Zhang (张岳) | Yang Liu (刘洋)
Proceedings of the 19th Chinese National Conference on Computational Linguistics

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THUMT: An Open-Source Toolkit for Neural Machine Translation
Zhixing Tan | Jiacheng Zhang | Xuancheng Huang | Gang Chen | Shuo Wang | Maosong Sun | Huanbo Luan | Yang Liu
Proceedings of the 14th Conference of the Association for Machine Translation in the Americas (Volume 1: Research Track)

2019

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Shared-Private Bilingual Word Embeddings for Neural Machine Translation
Xuebo Liu | Derek F. Wong | Yang Liu | Lidia S. Chao | Tong Xiao | Jingbo Zhu
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

Word embedding is central to neural machine translation (NMT), which has attracted intensive research interest in recent years. In NMT, the source embedding plays the role of the entrance while the target embedding acts as the terminal. These layers occupy most of the model parameters for representation learning. Furthermore, they indirectly interface via a soft-attention mechanism, which makes them comparatively isolated. In this paper, we propose shared-private bilingual word embeddings, which give a closer relationship between the source and target embeddings, and which also reduce the number of model parameters. For similar source and target words, their embeddings tend to share a part of the features and they cooperatively learn these common representation units. Experiments on 5 language pairs belonging to 6 different language families and written in 5 different alphabets demonstrate that the proposed model provides a significant performance boost over the strong baselines with dramatically fewer model parameters.

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Reducing Word Omission Errors in Neural Machine Translation: A Contrastive Learning Approach
Zonghan Yang | Yong Cheng | Yang Liu | Maosong Sun
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

While neural machine translation (NMT) has achieved remarkable success, NMT systems are prone to make word omission errors. In this work, we propose a contrastive learning approach to reducing word omission errors in NMT. The basic idea is to enable the NMT model to assign a higher probability to a ground-truth translation and a lower probability to an erroneous translation, which is automatically constructed from the ground-truth translation by omitting words. We design different types of negative examples depending on the number of omitted words, word frequency, and part of speech. Experiments on Chinese-to-English, German-to-English, and Russian-to-English translation tasks show that our approach is effective in reducing word omission errors and achieves better translation performance than three baseline methods.

2018

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Neural Network Methods for Natural Language Processing by Yoav Goldberg
Yang Liu | Meng Zhang
Computational Linguistics, Volume 44, Issue 1 - April 2018

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Error Analysis of Uyghur Name Tagging: Language-specific Techniques and Remaining Challenges
Halidanmu Abudukelimu | Abudoukelimu Abulizi | Boliang Zhang | Xiaoman Pan | Di Lu | Heng Ji | Yang Liu
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)

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Towards Robust Neural Machine Translation
Yong Cheng | Zhaopeng Tu | Fandong Meng | Junjie Zhai | Yang Liu
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Small perturbations in the input can severely distort intermediate representations and thus impact translation quality of neural machine translation (NMT) models. In this paper, we propose to improve the robustness of NMT models with adversarial stability training. The basic idea is to make both the encoder and decoder in NMT models robust against input perturbations by enabling them to behave similarly for the original input and its perturbed counterpart. Experimental results on Chinese-English, English-German and English-French translation tasks show that our approaches can not only achieve significant improvements over strong NMT systems but also improve the robustness of NMT models.

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Learning to Remember Translation History with a Continuous Cache
Zhaopeng Tu | Yang Liu | Shuming Shi | Tong Zhang
Transactions of the Association for Computational Linguistics, Volume 6

Existing neural machine translation (NMT) models generally translate sentences in isolation, missing the opportunity to take advantage of document-level information. In this work, we propose to augment NMT models with a very light-weight cache-like memory network, which stores recent hidden representations as translation history. The probability distribution over generated words is updated online depending on the translation history retrieved from the memory, endowing NMT models with the capability to dynamically adapt over time. Experiments on multiple domains with different topics and styles show the effectiveness of the proposed approach with negligible impact on the computational cost.

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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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Improving the Transformer Translation Model with Document-Level Context
Jiacheng Zhang | Huanbo Luan | Maosong Sun | Feifei Zhai | Jingfang Xu | Min Zhang | Yang Liu
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

Although the Transformer translation model (Vaswani et al., 2017) has achieved state-of-the-art performance in a variety of translation tasks, how to use document-level context to deal with discourse phenomena problematic for Transformer still remains a challenge. In this work, we extend the Transformer model with a new context encoder to represent document-level context, which is then incorporated into the original encoder and decoder. As large-scale document-level parallel corpora are usually not available, we introduce a two-step training method to take full advantage of abundant sentence-level parallel corpora and limited document-level parallel corpora. Experiments on the NIST Chinese-English datasets and the IWSLT French-English datasets show that our approach improves over Transformer significantly.

2017

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Visualizing and Understanding Neural Machine Translation
Yanzhuo Ding | Yang Liu | Huanbo Luan | Maosong Sun
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

While neural machine translation (NMT) has made remarkable progress in recent years, it is hard to interpret its internal workings due to the continuous representations and non-linearity of neural networks. In this work, we propose to use layer-wise relevance propagation (LRP) to compute the contribution of each contextual word to arbitrary hidden states in the attention-based encoder-decoder framework. We show that visualization with LRP helps to interpret the internal workings of NMT and analyze translation errors.

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Prior Knowledge Integration for Neural Machine Translation using Posterior Regularization
Jiacheng Zhang | Yang Liu | Huanbo Luan | Jingfang Xu | Maosong Sun
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Although neural machine translation has made significant progress recently, how to integrate multiple overlapping, arbitrary prior knowledge sources remains a challenge. In this work, we propose to use posterior regularization to provide a general framework for integrating prior knowledge into neural machine translation. We represent prior knowledge sources as features in a log-linear model, which guides the learning processing of the neural translation model. Experiments on Chinese-English dataset show that our approach leads to significant improvements.

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A Teacher-Student Framework for Zero-Resource Neural Machine Translation
Yun Chen | Yang Liu | Yong Cheng | Victor O.K. Li
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

While end-to-end neural machine translation (NMT) has made remarkable progress recently, it still suffers from the data scarcity problem for low-resource language pairs and domains. In this paper, we propose a method for zero-resource NMT by assuming that parallel sentences have close probabilities of generating a sentence in a third language. Based on the assumption, our method is able to train a source-to-target NMT model (“student”) without parallel corpora available guided by an existing pivot-to-target NMT model (“teacher”) on a source-pivot parallel corpus. Experimental results show that the proposed method significantly improves over a baseline pivot-based model by +3.0 BLEU points across various language pairs.

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Adversarial Training for Unsupervised Bilingual Lexicon Induction
Meng Zhang | Yang Liu | Huanbo Luan | Maosong Sun
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Word embeddings are well known to capture linguistic regularities of the language on which they are trained. Researchers also observe that these regularities can transfer across languages. However, previous endeavors to connect separate monolingual word embeddings typically require cross-lingual signals as supervision, either in the form of parallel corpus or seed lexicon. In this work, we show that such cross-lingual connection can actually be established without any form of supervision. We achieve this end by formulating the problem as a natural adversarial game, and investigating techniques that are crucial to successful training. We carry out evaluation on the unsupervised bilingual lexicon induction task. Even though this task appears intrinsically cross-lingual, we are able to demonstrate encouraging performance without any cross-lingual clues.

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Earth Mover’s Distance Minimization for Unsupervised Bilingual Lexicon Induction
Meng Zhang | Yang Liu | Huanbo Luan | Maosong Sun
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

Cross-lingual natural language processing hinges on the premise that there exists invariance across languages. At the word level, researchers have identified such invariance in the word embedding semantic spaces of different languages. However, in order to connect the separate spaces, cross-lingual supervision encoded in parallel data is typically required. In this paper, we attempt to establish the cross-lingual connection without relying on any cross-lingual supervision. By viewing word embedding spaces as distributions, we propose to minimize their earth mover’s distance, a measure of divergence between distributions. We demonstrate the success on the unsupervised bilingual lexicon induction task. In addition, we reveal an interesting finding that the earth mover’s distance shows potential as a measure of language difference.

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Context Gates for Neural Machine Translation
Zhaopeng Tu | Yang Liu | Zhengdong Lu | Xiaohua Liu | Hang Li
Transactions of the Association for Computational Linguistics, Volume 5

In neural machine translation (NMT), generation of a target word depends on both source and target contexts. We find that source contexts have a direct impact on the adequacy of a translation while target contexts affect the fluency. Intuitively, generation of a content word should rely more on the source context and generation of a functional word should rely more on the target context. Due to the lack of effective control over the influence from source and target contexts, conventional NMT tends to yield fluent but inadequate translations. To address this problem, we propose context gates which dynamically control the ratios at which source and target contexts contribute to the generation of target words. In this way, we can enhance both the adequacy and fluency of NMT with more careful control of the information flow from contexts. Experiments show that our approach significantly improves upon a standard attention-based NMT system by +2.3 BLEU points.

2016

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Modeling Coverage for Neural Machine Translation
Zhaopeng Tu | Zhengdong Lu | Yang Liu | Xiaohua Liu | Hang Li
Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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Agreement-based Learning of Parallel Lexicons and Phrases from Non-Parallel Corpora
Chunyang Liu | Yang Liu | Maosong Sun | Huanbo Luan | Heng Yu
Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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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)

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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)

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Inducing Bilingual Lexica From Non-Parallel Data With Earth Mover’s Distance Regularization
Meng Zhang | Yang Liu | Huanbo Luan | Yiqun Liu | Maosong Sun
Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers

Being able to induce word translations from non-parallel data is often a prerequisite for cross-lingual processing in resource-scarce languages and domains. Previous endeavors typically simplify this task by imposing the one-to-one translation assumption, which is too strong to hold for natural languages. We remove this constraint by introducing the Earth Mover’s Distance into the training of bilingual word embeddings. In this way, we take advantage of its capability to handle multiple alternative word translations in a natural form of regularization. Our approach shows significant and consistent improvements across four language pairs. We also demonstrate that our approach is particularly preferable in resource-scarce settings as it only requires a minimal seed lexicon.

2015

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Consistency-Aware Search for Word Alignment
Shiqi Shen | Yang Liu | Maosong Sun | Huanbo Luan
Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing

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Bilingual Correspondence Recursive Autoencoder for Statistical Machine Translation
Jinsong Su | Deyi Xiong | Biao Zhang | Yang Liu | Junfeng Yao | Min Zhang
Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing

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Generalized Agreement for Bidirectional Word Alignment
Chunyang Liu | Yang Liu | Maosong Sun | Huanbo Luan | Heng Yu
Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing

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A Context-Aware Topic Model for Statistical Machine Translation
Jinsong Su | Deyi Xiong | Yang Liu | Xianpei Han | Hongyu Lin | Junfeng Yao | Min Zhang
Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

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Learning Cross-lingual Word Embeddings via Matrix Co-factorization
Tianze Shi | Zhiyuan Liu | Yang Liu | Maosong Sun
Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)

2014

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Large-scale CCG Induction from the Groningen Meaning Bank
Sebastian Beschke | Yang Liu | Wolfgang Menzel
Proceedings of the ACL 2014 Workshop on Semantic Parsing

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A Neural Reordering Model for Phrase-based Translation
Peng Li | Yang Liu | Maosong Sun | Tatsuya Izuha | Dakun Zhang
Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: Technical Papers

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Query Lattice for Translation Retrieval
Meiping Dong | Yong Cheng | Yang Liu | Jia Xu | Maosong Sun | Tatsuya Izuha | Jie Hao
Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: Technical Papers

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Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics: Tutorials
Alex Fraser | Yang Liu
Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics: Tutorials

2013

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Recursive Autoencoders for ITG-Based Translation
Peng Li | Yang Liu | Maosong Sun
Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing

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A Shift-Reduce Parsing Algorithm for Phrase-based String-to-Dependency Translation
Yang Liu
Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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Statistical Machine Translation Improves Question Retrieval in Community Question Answering via Matrix Factorization
Guangyou Zhou | Fang Liu | Yang Liu | Shizhu He | Jun Zhao
Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

2012

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Left-to-Right Tree-to-String Decoding with Prediction
Yang Feng | Yang Liu | Qun Liu | Trevor Cohn
Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning

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Unsupervised Discriminative Induction of Synchronous Grammar for Machine Translation
Xinyan Xiao | Deyi Xiong | Yang Liu | Qun Liu | Shouxun Lin
Proceedings of COLING 2012

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A Beam Search Algorithm for ITG Word Alignment
Peng Li | Yang Liu | Maosong Sun
Proceedings of COLING 2012: Posters

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Combining Multiple Alignments to Improve Machine Translation
Zhaopeng Tu | Yang Liu | Yifan He | Josef van Genabith | Qun Liu | Shouxun Lin
Proceedings of COLING 2012: Posters

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THUTR: A Translation Retrieval System
Chunyang Liu | Qi Liu | Yang Liu | Maosong Sun
Proceedings of COLING 2012: Demonstration Papers

2011

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ETS: An Error Tolerable System for Coreference Resolution
Hao Xiong | Linfeng Song | Fandong Meng | Yang Liu | Qun Liu | Yajuan Lv
Proceedings of the Fifteenth Conference on Computational Natural Language Learning: Shared Task

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Fast Generation of Translation Forest for Large-Scale SMT Discriminative Training
Xinyan Xiao | Yang Liu | Qun Liu | Shouxun Lin
Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing

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Extracting Hierarchical Rules from a Weighted Alignment Matrix
Zhaopeng Tu | Yang Liu | Qun Liu | Shouxun Lin
Proceedings of 5th International Joint Conference on Natural Language Processing

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Maximum Rank Correlation Training for Statistical Machine Translation
Daqi Zheng | Yifan He | Yang Liu | Qun Liu
Proceedings of Machine Translation Summit XIII: Papers

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Adjoining Tree-to-String Translation
Yang Liu | Qun Liu | Yajuan Lü
Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies

2010

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Statistical Translation Model Based On Source Syntax Structure
Qun Liu | Yang Liu | Haitao Mi
Proceedings of the 24th Pacific Asia Conference on Language, Information and Computation

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Learning Lexicalized Reordering Models from Reordering Graphs
Jinsong Su | Yang Liu | Yajuan Lv | Haitao Mi | Qun Liu
Proceedings of the ACL 2010 Conference Short Papers

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Tree-Based and Forest-Based Translation
Yang Liu | Liang Huang
Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics: Tutorial Abstracts

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The ICT statistical machine translation system for IWSLT 2010
Hao Xiong | Jun Xie | Hui Yu | Kai Liu | Wei Luo | Haitao Mi | Yang Liu | Yajuan Lü | Qun Liu
Proceedings of the 7th International Workshop on Spoken Language Translation: Evaluation Campaign

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Joint Parsing and Translation
Yang Liu | Qun Liu
Proceedings of the 23rd International Conference on Computational Linguistics (Coling 2010)

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Dependency Forest for Statistical Machine Translation
Zhaopeng Tu | Yang Liu | Young-Sook Hwang | Qun Liu | Shouxun Lin
Proceedings of the 23rd International Conference on Computational Linguistics (Coling 2010)

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Joint Tokenization and Translation
Xinyan Xiao | Yang Liu | Young-Sook Hwang | Qun Liu | Shouxun Lin
Proceedings of the 23rd International Conference on Computational Linguistics (Coling 2010)

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An Efficient Shift-Reduce Decoding Algorithm for Phrased-Based Machine Translation
Yang Feng | Haitao Mi | Yang Liu | Qun Liu
Coling 2010: Posters

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Effective Constituent Projection across Languages
Wenbin Jiang | Yajuan Lv | Yang Liu | Qun Liu
Coling 2010: Posters

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Dependency-Based Bracketing Transduction Grammar for Statistical Machine Translation
Jinsong Su | Yang Liu | Haitao Mi | Hongmei Zhao | Yajuan Lv | Qun Liu
Coling 2010: Posters

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Discriminative Word Alignment by Linear Modeling
Yang Liu | Qun Liu | Shouxun Lin
Computational Linguistics, Volume 36, Issue 3 - September 2010

2009

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Improving Tree-to-Tree Translation with Packed Forests
Yang Liu | Yajuan Lü | Qun Liu
Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP

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Joint Decoding with Multiple Translation Models
Yang Liu | Haitao Mi | Yang Feng | Qun Liu
Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP

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Sub-Sentence Division for Tree-Based Machine Translation
Hao Xiong | Wenwen Xu | Haitao Mi | Yang Liu | Qun Liu
Proceedings of the ACL-IJCNLP 2009 Conference Short Papers

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Weighted Alignment Matrices for Statistical Machine Translation
Yang Liu | Tian Xia | Xinyan Xiao | Qun Liu
Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing

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Lattice-based System Combination for Statistical Machine Translation
Yang Feng | Yang Liu | Haitao Mi | Qun Liu | Yajuan Lü
Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing

2008

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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

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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.

2007

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Forest-to-String Statistical Translation Rules
Yang Liu | Yun Huang | Qun Liu | Shouxun Lin
Proceedings of the 45th Annual Meeting of the Association of Computational Linguistics

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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.

2006

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Tree-to-String Alignment Template for Statistical Machine Translation
Yang Liu | Qun Liu | Shouxun Lin
Proceedings of the 21st International Conference on Computational Linguistics and 44th Annual Meeting of the Association for Computational Linguistics

2005

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Log-Linear Models for Word Alignment
Yang Liu | Qun Liu | Shouxun Lin
Proceedings of the 43rd Annual Meeting of the Association for Computational Linguistics (ACL’05)

2002

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Building a Bilingual WordNet-Like Lexicon: The New Approach and Algorithms
Yang Liu | Shiwen Yu | Jiangsheng Yu
COLING 2002: The 17th International Conference on Computational Linguistics: Project Notes

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