Jian Zhang


2020

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Contextual Embeddings: When Are They Worth It?
Simran Arora | Avner May | Jian Zhang | Christopher Ré
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

We study the settings for which deep contextual embeddings (e.g., BERT) give large improvements in performance relative to classic pretrained embeddings (e.g., GloVe), and an even simpler baseline—random word embeddings—focusing on the impact of the training set size and the linguistic properties of the task. Surprisingly, we find that both of these simpler baselines can match contextual embeddings on industry-scale data, and often perform within 5 to 10% accuracy (absolute) on benchmark tasks. Furthermore, we identify properties of data for which contextual embeddings give particularly large gains: language containing complex structure, ambiguous word usage, and words unseen in training.

2016

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SQuAD: 100,000+ Questions for Machine Comprehension of Text
Pranav Rajpurkar | Jian Zhang | Konstantin Lopyrev | Percy Liang
Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing

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Fast Gated Neural Domain Adaptation: Language Model as a Case Study
Jian Zhang | Xiaofeng Wu | Andy Way | Qun Liu
Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers

Neural network training has been shown to be advantageous in many natural language processing applications, such as language modelling or machine translation. In this paper, we describe in detail a novel domain adaptation mechanism in neural network training. Instead of learning and adapting the neural network on millions of training sentences – which can be very time-consuming or even infeasible in some cases – we design a domain adaptation gating mechanism which can be used in recurrent neural networks and quickly learn the out-of-domain knowledge directly from the word vector representations with little speed overhead. In our experiments, we use the recurrent neural network language model (LM) as a case study. We show that the neural LM perplexity can be reduced by 7.395 and 12.011 using the proposed domain adaptation mechanism on the Penn Treebank and News data, respectively. Furthermore, we show that using the domain-adapted neural LM to re-rank the statistical machine translation n-best list on the French-to-English language pair can significantly improve translation quality.

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Topic-Informed Neural Machine Translation
Jian Zhang | Liangyou Li | Andy Way | Qun Liu
Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers

In recent years, neural machine translation (NMT) has demonstrated state-of-the-art machine translation (MT) performance. It is a new approach to MT, which tries to learn a set of parameters to maximize the conditional probability of target sentences given source sentences. In this paper, we present a novel approach to improve the translation performance in NMT by conveying topic knowledge during translation. The proposed topic-informed NMT can increase the likelihood of selecting words from the same topic and domain for translation. Experimentally, we demonstrate that topic-informed NMT can achieve a 1.15 (3.3% relative) and 1.67 (5.4% relative) absolute improvement in BLEU score on the Chinese-to-English language pair using NIST 2004 and 2005 test sets, respectively, compared to NMT without topic information.

2014

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A probabilistic feature-based fill-up for SMT
Jian Zhang | Liangyou Li | Andy Way | Qun Liu
Proceedings of the 11th Conference of the Association for Machine Translation in the Americas: MT Researchers Track

In this paper, we describe an effective translation model combination approach based on the estimation of a probabilistic Support Vector Machine (SVM). We collect domain knowledge from both in-domain and general-domain corpora inspired by a commonly used data selection algorithm, which we then use as features for the SVM training. Drawing on previous work on binary-featured phrase table fill-up (Nakov, 2008; Bisazza et al., 2011), we substitute the binary feature in the original work with our probabilistic domain-likeness feature. Later, we design two experiments to evaluate the proposed probabilistic feature-based approach on the French-to-English language pair using data provided at WMT07, WMT13 and IWLST11 translation tasks. Our experiments demonstrate that translation performance can gain significant improvements of up to +0.36 and +0.82 BLEU scores by using our probabilistic feature-based translation model fill-up approach compared with the binary featured fill-up approach in both experiments.

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Experiments in Medical Translation Shared Task at WMT 2014
Jian Zhang
Proceedings of the Ninth Workshop on Statistical Machine Translation

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Target-Centric Features for Translation Quality Estimation
Chris Hokamp | Iacer Calixto | Joachim Wagner | Jian Zhang
Proceedings of the Ninth Workshop on Statistical Machine Translation

2012

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PLUTO: Automated Solutions for Patent Translation
John Tinsley | Alexandru Ceausu | Jian Zhang
Proceedings of the Joint Workshop on Exploiting Synergies between Information Retrieval and Machine Translation (ESIRMT) and Hybrid Approaches to Machine Translation (HyTra)

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IPTranslator: Facilitating Patent Search with Machine Translation
John Tinsley | Alexandru Ceausu | Jian Zhang | Heidi Depraetere | Joeri Van de Walle
Proceedings of the 10th Conference of the Association for Machine Translation in the Americas: Commercial MT User Program

Intellectual Property professionals frequently need to carry out patent searches for a variety of reasons. During a typical search, they will retrieve approximately 30% of their results in a foreign language. The machine translation (MT) options currently available to patent searchers for these foreign-language patents vary in their quality, consistency, and general level of service. In this article, we introduce IPTranslator; an MT web service designed to cater for the needs of patent searchers. At the core of IPTranslator is a set of MT systems developed specifically for translating patent text. We describe the challenges faced in adapting MT technology to such a complex domain, and how the systems were evaluated to ensure that the quality was fit for purpose. Finally, we present the framework through which the IPTranslator service is delivered to users, and the value-adding features which address many of the issues with existing solutions.

2011

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Experiments on Domain Adaptation for Patent Machine Translation in the PLuTO project
Alexandru Ceauşu | John Tinsley | Jian Zhang | Andy Way
Proceedings of the 15th Annual conference of the European Association for Machine Translation

2010

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A Rhetorical Syntax-Driven Model for Speech Summarization
Jian Zhang | Pascale Fung
Proceedings of the 23rd International Conference on Computational Linguistics (Coling 2010)

2009

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Active Learning of Extractive Reference Summaries for Lecture Speech Summarization
Jian Zhang | Pascale Fung
Proceedings of the 2nd Workshop on Building and Using Comparable Corpora: from Parallel to Non-parallel Corpora (BUCC)

2007

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Speech Summarization Without Lexical Features for Mandarin Broadcast News
Jian Zhang | Pascale Fung
Human Language Technologies 2007: The Conference of the North American Chapter of the Association for Computational Linguistics; Companion Volume, Short Papers

2001

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Improving the Effectiveness of Information Retrieval with Clustering and Fusion
Jian Zhang | Jianfeng Gao | Ming Zhou | Jiaxing Wang
International Journal of Computational Linguistics & Chinese Language Processing, Volume 6, Number 1, February 2001: Special Issue on Natural Language Processing Researches in MSRA

2000

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Extraction of Chinese Compound Words - An Experimental Study on a Very Large Corpus
Jian Zhang | Jianfeng Gao | Ming Zhou
Second Chinese Language Processing Workshop