Yujie Zhang


2021

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基于多任务标签一致性机制的中文命名实体识别(Chinese Named Entity Recognition based on Multi-task Label Consistency Mechanism)
Shuning Lv (吕书宁) | Jian Liu (刘健) | Jinan Xu (徐金安) | Yufeng Chen (陈钰枫) | Yujie Zhang (张玉洁)
Proceedings of the 20th Chinese National Conference on Computational Linguistics

实体边界预测对中文命名实体识别至关重要。现有研究为改善边界识别效果提出的多任务学习方法仅考虑与分词任务结合,缺少多任务标签训练数据,无法学到任务的标签一致性关系。本文提出一种新的基于多任务标签一致性机制的中文命名实体识别方法:将分词和词性信息融入命名实体识别模型,使三种任务联合训练;建立基于标签一致性机制的多任务学习模式,来捕获标签一致性关系及学习多任务表示。全样本和小样本实验表明了方法的有效性。

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融合外部知识的开放域复述模板获取方法(An Open Domain Paraphrasing Template Acquisition Method Based on External Knowledge)
Bo Jin (金波) | Mingtong Liu (刘明童) | Yujie Zhang (张玉洁) | Jinan Xu (徐金安) | Yufeng Chen (陈钰枫)
Proceedings of the 20th Chinese National Conference on Computational Linguistics

如何挖掘语言资源中丰富的复述模板,是复述研究中的一项重要任务。已有方法在人工给定种子实体对的基础上,利用实体关系,通过自举迭代方式,从开放域获取复述模板,规避对平行语料或可比语料的依赖,但是该方法需人工给定实体对,实体关系受限;在迭代过程中语义会发生偏移,影响获取质量。针对这些问题,我们考虑知识库中包含描述特定语义关系的实体对(即关系三元组),提出融合外部知识的开放域复述模板自动获取方法。首先,将关系三元组与开放域文本对齐,获取关系对应文本,并将文本中语义丰富部分泛化成变量槽,获取关系模板;接着设计模板表示方法,本文利用预训练语言模型,在模板表示中融合变量槽语义;最后,根据获得的模板表示,设计自动聚类与筛选方法,获取高精度的复述模板。在融合自动评测与人工评测的评价方法下,实验结果表明,本文提出的方法实现了在开放域数据上复述模板的自动泛化与获取,能够获得质量高、语义一致的复述模板。

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Syntactically-Informed Unsupervised Paraphrasing with Non-Parallel Data
Erguang Yang | Mingtong Liu | Deyi Xiong | Yujie Zhang | Yao Meng | Changjian Hu | Jinan Xu | Yufeng Chen
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

Previous works on syntactically controlled paraphrase generation heavily rely on large-scale parallel paraphrase data that is not easily available for many languages and domains. In this paper, we take this research direction to the extreme and investigate whether it is possible to learn syntactically controlled paraphrase generation with nonparallel data. We propose a syntactically-informed unsupervised paraphrasing model based on conditional variational auto-encoder (VAE) which can generate texts in a specified syntactic structure. Particularly, we design a two-stage learning method to effectively train the model using non-parallel data. The conditional VAE is trained to reconstruct the input sentence according to the given input and its syntactic structure. Furthermore, to improve the syntactic controllability and semantic consistency of the pre-trained conditional VAE, we fine-tune it using syntax controlling and cycle reconstruction learning objectives, and employ Gumbel-Softmax to combine these new learning objectives. Experiment results demonstrate that the proposed model trained only on non-parallel data is capable of generating diverse paraphrases with specified syntactic structure. Additionally, we validate the effectiveness of our method for generating syntactically adversarial examples on the sentiment analysis task.

2020

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A Learning-Exploring Method to Generate Diverse Paraphrases with Multi-Objective Deep Reinforcement Learning
Mingtong Liu | Erguang Yang | Deyi Xiong | Yujie Zhang | Yao Meng | Changjian Hu | Jinan Xu | Yufeng Chen
Proceedings of the 28th International Conference on Computational Linguistics

Paraphrase generation (PG) is of great importance to many downstream tasks in natural language processing. Diversity is an essential nature to PG for enhancing generalization capability and robustness of downstream applications. Recently, neural sequence-to-sequence (Seq2Seq) models have shown promising results in PG. However, traditional model training for PG focuses on optimizing model prediction against single reference and employs cross-entropy loss, which objective is unable to encourage model to generate diverse paraphrases. In this work, we present a novel approach with multi-objective learning to PG. We propose a learning-exploring method to generate sentences as learning objectives from the learned data distribution, and employ reinforcement learning to combine these new learning objectives for model training. We first design a sample-based algorithm to explore diverse sentences. Then we introduce several reward functions to evaluate the sampled sentences as learning signals in terms of expressive diversity and semantic fidelity, aiming to generate diverse and high-quality paraphrases. To effectively optimize model performance satisfying different evaluating aspects, we use a GradNorm-based algorithm that automatically balances these training objectives. Experiments and analyses on Quora and Twitter datasets demonstrate that our proposed method not only gains a significant increase in diversity but also improves generation quality over several state-of-the-art baselines.

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基于图神经网络的汉语依存分析和语义组合计算联合模型(Joint Learning Chinese Dependency Parsing and Semantic Composition based on Graph Neural Network)
Kai Wang (汪凯) | Mingtong Liu (刘明童) | Yuanmeng Chen (陈圆梦) | Yujie Zhang (张玉洁) | Jinan Xu (徐金安) | Yufeng Chen (陈钰枫)
Proceedings of the 19th Chinese National Conference on Computational Linguistics

组合原则表明句子的语义由其构成成分的语义按照一定规则组合而成, 由此基于句法结构的语义组合计算一直是一个重要的探索方向,其中采用树结构的组合计算方法最具有代表性。但是该方法难以应用于大规模数据处理,主要问题是其语义组合的顺序依赖于具体树的结构,无法实现并行处理。本文提出一种基于图的依存句法分析和语义组合计算的联合框架,并借助复述识别任务训练语义组合模型和句法分析模型。一方面图模型可以在训练和预测阶段采用并行处理,极大缩短计算时间;另一方面联合句法分析的语义组合框架不必依赖外部句法分析器,同时两个任务的联合学习可使语义表示同时学习句法结构和语义的上下文信息。我们在公开汉语复述识别数据集LCQMC上进行评测,实验结果显示准确率接近树结构组合方法,达到79.54%,而预测速度提升高达30倍。

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联合依存分析的汉语语义组合模型(Chinese Semantic Composition Model with Dependency Parsing)
Yuanmeng Chen (陈圆梦) | Yujie Zhang (张玉洁) | Jinan Xu (徐金安) | Yufeng Chen (陈钰枫)
Proceedings of the 19th Chinese National Conference on Computational Linguistics

在语义组合方法中,结构化方法强调以结构信息指导词义表示的组合方式。现有结构化语义组合方法使用外部分析器获取句法结构信息,导致句法分析与语义组合相互割裂,句法分析的精度严重制约语义组合模型的性能,且训练数据领域不一致等问题会进一步加剧性能的下降。对此,本文提出联合依存分析的语义组合模型,将依存分析与语义组合进行联合,一方面在训练语义组合模型时对依存分析模型进行微调,使其能够更适应语义组合模型使用的训练数据的领域特点;另一方面,在语义组合部分加入依存分析的中间信息表示,获取更丰富的结构信息和语义信息,以此来降低语义组合模型对依存分析错误结果的敏感度,提升模型的鲁棒性。我们以汉语为具体研究对象,将语义组合模型用于复述识别任务,并在CTB5汉语依存分析数据和LCQMC汉语复述识别数据上验证本文提出的模型。实验结果显示,本文所提方法在复述识别任务上的预测正确率和F1值上分别达到76.81%和78.03%;我们进一步设计实验对联合学习和中间信息利用的有效性进行验证,并与相关代表性工作进行了对比分析。

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A Joint Model for Graph-based Chinese Dependency Parsing
Xingchen Li | Mingtong Liu | Yujie Zhang | Jinan Xu | Yufeng Chen
Proceedings of the 19th Chinese National Conference on Computational Linguistics

In Chinese dependency parsing, the joint model of word segmentation, POS tagging and dependency parsing has become the mainstream framework because it can eliminate error propagation and share knowledge, where the transition-based model with feature templates maintains the best performance. Recently, the graph-based joint model (Yan et al., 2019) on word segmentation and dependency parsing has achieved better performance, demonstrating the advantages of the graph-based models. However, this work can not provide POS information for downstream tasks, and the POS tagging task was proved to be helpful to the dependency parsing according to the research of the transition-based model. Therefore, we propose a graph-based joint model for Chinese word segmentation, POS tagging and dependency parsing. We designed a charater-level POS tagging task, and then train it jointly with the model of Yan et al. (2019). We adopt two methods of joint POS tagging task, one is by sharing parameters, the other is by using tag attention mechanism, which enables the three tasks to better share intermediate information and improve each other’s performance. The experimental results on the Penn Chinese treebank (CTB5) show that our proposed joint model improved by 0.38% on dependency parsing than the model of Yan et al. (2019). Compared with the best transition-based joint model, our model improved by 0.18%, 0.35% and 5.99% respectively in terms of word segmentation, POS tagging and dependency parsing.

2019

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Original Semantics-Oriented Attention and Deep Fusion Network for Sentence Matching
Mingtong Liu | Yujie Zhang | Jinan Xu | Yufeng 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)

Sentence matching is a key issue in natural language inference and paraphrase identification. Despite the recent progress on multi-layered neural network with cross sentence attention, one sentence learns attention to the intermediate representations of another sentence, which are propagated from preceding layers and therefore are uncertain and unstable for matching, particularly at the risk of error propagation. In this paper, we present an original semantics-oriented attention and deep fusion network (OSOA-DFN) for sentence matching. Unlike existing models, each attention layer of OSOA-DFN is oriented to the original semantic representation of another sentence, which captures the relevant information from a fixed matching target. The multiple attention layers allow one sentence to repeatedly read the important information of another sentence for better matching. We then additionally design deep fusion to propagate the attention information at each matching layer. At last, we introduce a self-attention mechanism to capture global context to enhance attention-aware representation within each sentence. Experiment results on three sentence matching benchmark datasets SNLI, SciTail and Quora show that OSOA-DFN has the ability to model sentence matching more precisely.

2016

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System Description of bjtu_nlp Neural Machine Translation System
Shaotong Li | JinAn Xu | Yufeng Chen | Yujie Zhang
Proceedings of the 3rd Workshop on Asian Translation (WAT2016)

This paper presents our machine translation system that developed for the WAT2016 evalua-tion tasks of ja-en, ja-zh, en-ja, zh-ja, JPCja-en, JPCja-zh, JPCen-ja, JPCzh-ja. We build our system based on encoder–decoder framework by integrating recurrent neural network (RNN) and gate recurrent unit (GRU), and we also adopt an attention mechanism for solving the problem of information loss. Additionally, we propose a simple translation-specific approach to resolve the unknown word translation problem. Experimental results show that our system performs better than the baseline statistical machine translation (SMT) systems in each task. Moreover, it shows that our proposed approach of unknown word translation performs effec-tively improvement of translation results.

2015

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Integrating Case Frame into Japanese to Chinese Hierarchical Phrase-based Translation Model
Jinan Xu | Jiangming Liu | Yufeng Chen | Yujie Zhang | Fang Ming | Shaotong Li
Proceedings of the 1st Workshop on Semantics-Driven Statistical Machine Translation (S2MT 2015)

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A Hybrid Transliteration Model for Chinese/English Named Entities —BJTU-NLP Report for the 5th Named Entities Workshop
Dandan Wang | Xiaohui Yang | Jinan Xu | Yufeng Chen | Nan Wang | Bojia Liu | Jian Yang | Yujie Zhang
Proceedings of the Fifth Named Entity Workshop

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A Dependency-to-String Model for Chinese-Japanese SMT System
Hua Shan | Yujie Zhang | Lu Bai | Te Luo
Proceedings of the 2nd Workshop on Asian Translation (WAT2015)

2014

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System Description: Dependency-based Pre-ordering for Japanese-Chinese Machine Translation
Jingsheng Cai | Yujie Zhang | Hua Shan | Jinan Xu
Proceedings of the 1st Workshop on Asian Translation (WAT2014)

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Dependency-based Pre-ordering for Chinese-English Machine Translation
Jingsheng Cai | Masao Utiyama | Eiichiro Sumita | Yujie Zhang
Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

2013

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An Approach of Hybrid Hierarchical Structure for Word Similarity Computing by HowNet
Jiangming Liu | Jinan Xu | Yujie Zhang
Proceedings of the Sixth International Joint Conference on Natural Language Processing

2011

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SMT Helps Bitext Dependency Parsing
Wenliang Chen | Jun’ichi Kazama | Min Zhang | Yoshimasa Tsuruoka | Yujie Zhang | Yiou Wang | Kentaro Torisawa | Haizhou Li
Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing

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Improving Chinese Word Segmentation and POS Tagging with Semi-supervised Methods Using Large Auto-Analyzed Data
Yiou Wang | Jun’ichi Kazama | Yoshimasa Tsuruoka | Wenliang Chen | Yujie Zhang | Kentaro Torisawa
Proceedings of 5th International Joint Conference on Natural Language Processing

2008

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Word Alignment Annotation in a Japanese-Chinese Parallel Corpus
Yujie Zhang | Zhulong Wang | Kiyotaka Uchimoto | Qing Ma | Hitoshi Isahara
Proceedings of the Sixth International Conference on Language Resources and Evaluation (LREC'08)

Parallel corpora are critical resources for machine translation research and development since parallel corpora contain translation equivalences of various granularities. Manual annotation of word & phrase alignment is of significance to provide gold-standard for developing and evaluating both example-based machine translation model and statistical machine translation model. This paper presents the work of word & phrase alignment annotation in the NICT Japanese-Chinese parallel corpus, which is constructed at the National Institute of Information and Communications Technology (NICT). We describe the specification of word alignment annotation and the tools specially developed for the manual annotation. The manual annotation on 17,000 sentence pairs has been completed. We examined the manually annotated word alignment data and extracted translation knowledge from the word & phrase aligned corpus.

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Dependency Parsing with Short Dependency Relations in Unlabeled Data
Wenliang Chen | Daisuke Kawahara | Kiyotaka Uchimoto | Yujie Zhang | Hitoshi Isahara
Proceedings of the Third International Joint Conference on Natural Language Processing: Volume-I

2007

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Building Japanese-Chinese translation dictionary based on EDR Japanese-English bilingual dictionary
Yujie Zhang | Qing Ma | Hitoshi Isahara
Proceedings of Machine Translation Summit XI: Papers

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A Two-Stage Parser for Multilingual Dependency Parsing
Wenliang Chen | Yujie Zhang | Hitoshi Isahara
Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CoNLL)

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Automatic Evaluation of Machine Translation Based on Rate of Accomplishment of Sub-Goals
Kiyotaka Uchimoto | Katsunori Kotani | Yujie Zhang | Hitoshi Isahara
Human Language Technologies 2007: The Conference of the North American Chapter of the Association for Computational Linguistics; Proceedings of the Main Conference

2006

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An Empirical Study of Chinese Chunking
Wenliang Chen | Yujie Zhang | Hitoshi Isahara
Proceedings of the COLING/ACL 2006 Main Conference Poster Sessions

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Chinese Named Entity Recognition with Conditional Random Fields
Wenliang Chen | Yujie Zhang | Hitoshi Isahara
Proceedings of the Fifth SIGHAN Workshop on Chinese Language Processing

2005

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Building an Annotated Japanese-Chinese Parallel Corpus - A Part of NICT Multilingual Corpora
Yujie Zhang | Kiyotaka Uchimoto | Qing Ma | Hitoshi Isahara
Companion Volume to the Proceedings of Conference including Posters/Demos and tutorial abstracts

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Introduction to China’s HTRDP Machine Translation Evaluation
Qun Liu | Hongxu Hou | Shouxun Lin | Yueliang Qian | Yujie Zhang | Hitoshi Isahara
Proceedings of Machine Translation Summit X: Invited papers

Since 1994, China’s HTRDP machine translation evaluation has been conducted for five times. Systems of various translation directions between Chinese, English, Japanese and French have been tested. Both human evaluation and automatic evaluation are conducted in HTRDP evaluation. In recent years, the evaluation was organized jointly with NICT of Japan. This paper introduces some details of this evaluation.

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Building an Annotated Japanese-Chinese Parallel Corpus – A Part of NICT Multilingual Corpora
Yujie Zhang | Kiyotaka Uchimoto | Qing Ma | Hitoshi Isahara
Proceedings of Machine Translation Summit X: Papers

We are constricting a Japanese-Chinese parallel corpus, which is a part of the NICT Multilingual Corpora. The corpus is general domain, of large scale of about 40,000 sentence pairs, long sentences, annotated with detailed information and high quality. To the best of our knowledge, this will be the first annotated Japanese-Chinese parallel corpus in the world. We created the corpus by selecting Japanese sentences from Mainichi Newspaper and then manually translating them into Chinese. We then annotated the corpus with morphological and syntactic structures and alignments at word and phrase levels. This paper describes the specification in human translation and detailed information annotation, and the tools we developed in the project. The experience we obtained and points we paid special attentions are also introduced for share with other researches in corpora construction.

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A Multi-aligner for Japanese-Chinese Parallel Corpora
Yujie Zhang | Qun Liu | Qing Ma | Hitoshi Isahara
Proceedings of Machine Translation Summit X: Papers

Automatic word alignment is an important technology for extracting translation knowledge from parallel corpora. However, automatic techniques cannot resolve this problem completely because of variances in translations. We therefore need to investigate the performance potential of automatic word alignment and then decide how to suitably apply it. In this paper we first propose a lexical knowledge-based approach to word alignment on a Japanese-Chinese corpus. Then we evaluate the performance of the proposed approach on the corpus. At the same time we also apply a statistics-based approach, the well-known toolkit GIZA++, to the same test data. Through comparison of the performances of the two approaches, we propose a multi-aligner, exploiting the lexical knowledge-based aligner and the statistics-based aligner at the same time. Quantitative results confirmed the effectiveness of the multi-aligner.

2004

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Multilingual Aligned Parallel Treebank Corpus Reflecting Contextual Information and Its Applications
Kiyotaka Uchimoto | Yujie Zhang | Kiyoshi Sudo | Masaki Murata | Satoshi Sekine | Hitoshi Isahara
Proceedings of the Workshop on Multilingual Linguistic Resources

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Acquiring Compound Word Translations both Automatically and Dynamically
Yujie Zhang | Hitoshi Isahara
Proceedings of the 18th Pacific Asia Conference on Language, Information and Computation

2003

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Semantic Maps for Word Alignment in Bilingual Parallel Corpora
Qing Ma | Yujie Zhang | Masaki Murata | Hitoshi Isahara
Proceedings of the Second SIGHAN Workshop on Chinese Language Processing

2002

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Paraphrasing of Chinese Utterances
Yujie Zhang | Kazuhide Yamamoto
COLING 2002: The 19th International Conference on Computational Linguistics

1999

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A Classification Tree Approach to Automatic Segmentation of Japanese Compound Sentences
Yujie Zhang | Kazuhiko Ozeki
Proceedings of the 13th Pacific Asia Conference on Language, Information and Computation

1998

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Automatic Bunsetsu Segmentation of Japanese Sentences Using a Classification Tree
Yujie Zhang | Kazuhiko Ozeki
Proceedings of the 12th Pacific Asia Conference on Language, Information and Computation