Bo Xu


2020

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基于BERT的端到端中文篇章事件抽取(A BERT-based End-to-End Model for Chinese Document-level Event Extraction)
Hongkuan Zhang (张洪宽) | Hui Song (宋晖) | Shuyi Wang (王舒怡) | Bo Xu (徐波)
Proceedings of the 19th Chinese National Conference on Computational Linguistics

篇章级事件抽取研究从整篇文档中检测事件,识别出事件包含的元素并赋予每个元素特定的角色。本文针对限定领域的中文文档提出了基于BERT的端到端模型,在模型的元素和角色识别中依次引入前序层输出的事件类型以及实体嵌入表示,增强文本的事件、元素和角色关联表示,提高篇章中各事件所属元素的识别精度。在此基础上利用标题信息和事件五元组的嵌入式表示,实现主从事件的划分及元素融合。实验证明本文的方法与现有工作相比具有明显的提升。

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Knowledge Aware Emotion Recognition in Textual Conversations via Multi-Task Incremental Transformer
Duzhen Zhang | Xiuyi Chen | Shuang Xu | Bo Xu
Proceedings of the 28th International Conference on Computational Linguistics

Emotion recognition in textual conversations (ERTC) plays an important role in a wide range of applications, such as opinion mining, recommender systems, and so on. ERTC, however, is a challenging task. For one thing, speakers often rely on the context and commonsense knowledge to express emotions; for another, most utterances contain neutral emotion in conversations, as a result, the confusion between a few non-neutral utterances and much more neutral ones restrains the emotion recognition performance. In this paper, we propose a novel Knowledge Aware Incremental Transformer with Multi-task Learning (KAITML) to address these challenges. Firstly, we devise a dual-level graph attention mechanism to leverage commonsense knowledge, which augments the semantic information of the utterance. Then we apply the Incremental Transformer to encode multi-turn contextual utterances. Moreover, we are the first to introduce multi-task learning to alleviate the aforementioned confusion and thus further improve the emotion recognition performance. Extensive experimental results show that our KAITML model outperforms the state-of-the-art models across five benchmark datasets.

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Bridging the Gap between Prior and Posterior Knowledge Selection for Knowledge-Grounded Dialogue Generation
Xiuyi Chen | Fandong Meng | Peng Li | Feilong Chen | Shuang Xu | Bo Xu | Jie Zhou
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Knowledge selection plays an important role in knowledge-grounded dialogue, which is a challenging task to generate more informative responses by leveraging external knowledge. Recently, latent variable models have been proposed to deal with the diversity of knowledge selection by using both prior and posterior distributions over knowledge and achieve promising performance. However, these models suffer from a huge gap between prior and posterior knowledge selection. Firstly, the prior selection module may not learn to select knowledge properly because of lacking the necessary posterior information. Secondly, latent variable models suffer from the exposure bias that dialogue generation is based on the knowledge selected from the posterior distribution at training but from the prior distribution at inference. Here, we deal with these issues on two aspects: (1) We enhance the prior selection module with the necessary posterior information obtained from the specially designed Posterior Information Prediction Module (PIPM); (2) We propose a Knowledge Distillation Based Training Strategy (KDBTS) to train the decoder with the knowledge selected from the prior distribution, removing the exposure bias of knowledge selection. Experimental results on two knowledge-grounded dialogue datasets show that both PIPM and KDBTS achieve performance improvement over the state-of-the-art latent variable model and their combination shows further improvement.

2019

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A Working Memory Model for Task-oriented Dialog Response Generation
Xiuyi Chen | Jiaming Xu | Bo Xu
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

Recently, to incorporate external Knowledge Base (KB) information, one form of world knowledge, several end-to-end task-oriented dialog systems have been proposed. These models, however, tend to confound the dialog history with KB tuples and simply store them into one memory. Inspired by the psychological studies on working memory, we propose a working memory model (WMM2Seq) for dialog response generation. Our WMM2Seq adopts a working memory to interact with two separated long-term memories, which are the episodic memory for memorizing dialog history and the semantic memory for storing KB tuples. The working memory consists of a central executive to attend to the aforementioned memories, and a short-term storage system to store the “activated” contents from the long-term memories. Furthermore, we introduce a context-sensitive perceptual process for the token representations of dialog history, and then feed them into the episodic memory. Extensive experiments on two task-oriented dialog datasets demonstrate that our WMM2Seq significantly outperforms the state-of-the-art results in several evaluation metrics.

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Transformer-Based Capsule Network For Stock Movement Prediction
Jintao Liu | Hongfei Lin | Xikai Liu | Bo Xu | Yuqi Ren | Yufeng Diao | Liang Yang
Proceedings of the First Workshop on Financial Technology and Natural Language Processing

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The World in My Mind: Visual Dialog with Adversarial Multi-modal Feature Encoding
Yiqun Yao | Jiaming Xu | Bo Xu
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)

Visual Dialog is a multi-modal task that requires a model to participate in a multi-turn human dialog grounded on an image, and generate correct, human-like responses. In this paper, we propose a novel Adversarial Multi-modal Feature Encoding (AMFE) framework for effective and robust auxiliary training of visual dialog systems. AMFE can force the language-encoding part of a model to generate hidden states in a distribution closely related to the distribution of real-world images, resulting in language features containing general knowledge from both modalities by nature, which can help generate both more correct and more general responses with reasonably low time cost. Experimental results show that AMFE can steadily bring performance gains to different models on different scales of data. Our method outperforms both the supervised learning baselines and other fine-tuning methods, achieving state-of-the-art results on most metrics of VisDial v0.5/v0.9 generative tasks.

2018

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Improving Neural Machine Translation with Conditional Sequence Generative Adversarial Nets
Zhen Yang | Wei Chen | Feng Wang | Bo Xu
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)

This paper proposes an approach for applying GANs to NMT. We build a conditional sequence generative adversarial net which comprises of two adversarial sub models, a generator and a discriminator. The generator aims to generate sentences which are hard to be discriminated from human-translated sentences ( i.e., the golden target sentences); And the discriminator makes efforts to discriminate the machine-generated sentences from human-translated ones. The two sub models play a mini-max game and achieve the win-win situation when they reach a Nash Equilibrium. Additionally, the static sentence-level BLEU is utilized as the reinforced objective for the generator, which biases the generation towards high BLEU points. During training, both the dynamic discriminator and the static BLEU objective are employed to evaluate the generated sentences and feedback the evaluations to guide the learning of the generator. Experimental results show that the proposed model consistently outperforms the traditional RNNSearch and the newly emerged state-of-the-art Transformer on English-German and Chinese-English translation tasks.

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Cascaded Mutual Modulation for Visual Reasoning
Yiqun Yao | Jiaming Xu | Feng Wang | Bo Xu
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

Visual reasoning is a special visual question answering problem that is multi-step and compositional by nature, and also requires intensive text-vision interactions. We propose CMM: Cascaded Mutual Modulation as a novel end-to-end visual reasoning model. CMM includes a multi-step comprehension process for both question and image. In each step, we use a Feature-wise Linear Modulation (FiLM) technique to enable textual/visual pipeline to mutually control each other. Experiments show that CMM significantly outperforms most related models, and reach state-of-the-arts on two visual reasoning benchmarks: CLEVR and NLVR, collected from both synthetic and natural languages. Ablation studies confirm the effectiveness of CMM to comprehend natural language logics under the guidence of images. Our code is available at https://github.com/FlamingHorizon/CMM-VR.

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WECA: A WordNet-Encoded Collocation-Attention Network for Homographic Pun Recognition
Yufeng Diao | Hongfei Lin | Di Wu | Liang Yang | Kan Xu | Zhihao Yang | Jian Wang | Shaowu Zhang | Bo Xu | Dongyu Zhang
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

Homographic puns have a long history in human writing, widely used in written and spoken literature, which usually occur in a certain syntactic or stylistic structure. How to recognize homographic puns is an important research. However, homographic pun recognition does not solve very well in existing work. In this work, we first use WordNet to understand and expand word embedding for settling the polysemy of homographic puns, and then propose a WordNet-Encoded Collocation-Attention network model (WECA) which combined with the context weights for recognizing the puns. Our experiments on the SemEval2017 Task7 and Pun of the Day demonstrate that the proposed model is able to distinguish between homographic pun and non-homographic pun texts. We show the effectiveness of the model to present the capability of choosing qualitatively informative words. The results show that our model achieves the state-of-the-art performance on homographic puns recognition.

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Semi-Supervised Disfluency Detection
Feng Wang | Wei Chen | Zhen Yang | Qianqian Dong | Shuang Xu | Bo Xu
Proceedings of the 27th International Conference on Computational Linguistics

While the disfluency detection has achieved notable success in the past years, it still severely suffers from the data scarcity. To tackle this problem, we propose a novel semi-supervised approach which can utilize large amounts of unlabelled data. In this work, a light-weight neural net is proposed to extract the hidden features based solely on self-attention without any Recurrent Neural Network (RNN) or Convolutional Neural Network (CNN). In addition, we use the unlabelled corpus to enhance the performance. Besides, the Generative Adversarial Network (GAN) training is applied to enforce the similar distribution between the labelled and unlabelled data. The experimental results show that our approach achieves significant improvements over strong baselines.

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Unsupervised Neural Machine Translation with Weight Sharing
Zhen Yang | Wei Chen | Feng Wang | Bo Xu
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Unsupervised neural machine translation (NMT) is a recently proposed approach for machine translation which aims to train the model without using any labeled data. The models proposed for unsupervised NMT often use only one shared encoder to map the pairs of sentences from different languages to a shared-latent space, which is weak in keeping the unique and internal characteristics of each language, such as the style, terminology, and sentence structure. To address this issue, we introduce an extension by utilizing two independent encoders but sharing some partial weights which are responsible for extracting high-level representations of the input sentences. Besides, two different generative adversarial networks (GANs), namely the local GAN and global GAN, are proposed to enhance the cross-language translation. With this new approach, we achieve significant improvements on English-German, English-French and Chinese-to-English translation tasks.

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Construction of a Chinese Corpus for the Analysis of the Emotionality of Metaphorical Expressions
Dongyu Zhang | Hongfei Lin | Liang Yang | Shaowu Zhang | Bo Xu
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

Metaphors are frequently used to convey emotions. However, there is little research on the construction of metaphor corpora annotated with emotion for the analysis of emotionality of metaphorical expressions. Furthermore, most studies focus on English, and few in other languages, particularly Sino-Tibetan languages such as Chinese, for emotion analysis from metaphorical texts, although there are likely to be many differences in emotional expressions of metaphorical usages across different languages. We therefore construct a significant new corpus on metaphor, with 5,605 manually annotated sentences in Chinese. We present an annotation scheme that contains annotations of linguistic metaphors, emotional categories (joy, anger, sadness, fear, love, disgust and surprise), and intensity. The annotation agreement analyses for multiple annotators are described. We also use the corpus to explore and analyze the emotionality of metaphors. To the best of our knowledge, this is the first relatively large metaphor corpus with an annotation of emotions in Chinese.

2017

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Joint Extraction of Entities and Relations Based on a Novel Tagging Scheme
Suncong Zheng | Feng Wang | Hongyun Bao | Yuexing Hao | Peng Zhou | Bo Xu
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Joint extraction of entities and relations is an important task in information extraction. To tackle this problem, we firstly propose a novel tagging scheme that can convert the joint extraction task to a tagging problem.. Then, based on our tagging scheme, we study different end-to-end models to extract entities and their relations directly, without identifying entities and relations separately. We conduct experiments on a public dataset produced by distant supervision method and the experimental results show that the tagging based methods are better than most of the existing pipelined and joint learning methods. What’s more, the end-to-end model proposed in this paper, achieves the best results on the public dataset.

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Convolutional Neural Network with Word Embeddings for Chinese Word Segmentation
Chunqi Wang | Bo Xu
Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

Character-based sequence labeling framework is flexible and efficient for Chinese word segmentation (CWS). Recently, many character-based neural models have been applied to CWS. While they obtain good performance, they have two obvious weaknesses. The first is that they heavily rely on manually designed bigram feature, i.e. they are not good at capturing n-gram features automatically. The second is that they make no use of full word information. For the first weakness, we propose a convolutional neural model, which is able to capture rich n-gram features without any feature engineering. For the second one, we propose an effective approach to integrate the proposed model with word embeddings. We evaluate the model on two benchmark datasets: PKU and MSR. Without any feature engineering, the model obtains competitive performance — 95.7% on PKU and 97.3% on MSR. Armed with word embeddings, the model achieves state-of-the-art performance on both datasets — 96.5% on PKU and 98.0% on MSR, without using any external labeled resource.

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Towards Compact and Fast Neural Machine Translation Using a Combined Method
Xiaowei Zhang | Wei Chen | Feng Wang | Shuang Xu | Bo Xu
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

Neural Machine Translation (NMT) lays intensive burden on computation and memory cost. It is a challenge to deploy NMT models on the devices with limited computation and memory budgets. This paper presents a four stage pipeline to compress model and speed up the decoding for NMT. Our method first introduces a compact architecture based on convolutional encoder and weight shared embeddings. Then weight pruning is applied to obtain a sparse model. Next, we propose a fast sequence interpolation approach which enables the greedy decoding to achieve performance on par with the beam search. Hence, the time-consuming beam search can be replaced by simple greedy decoding. Finally, vocabulary selection is used to reduce the computation of softmax layer. Our final model achieves 10 times speedup, 17 times parameters reduction, less than 35MB storage size and comparable performance compared to the baseline model.

2016

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Hierarchical Memory Networks for Answer Selection on Unknown Words
Jiaming Xu | Jing Shi | Yiqun Yao | Suncong Zheng | Bo Xu | Bo Xu
Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers

Recently, end-to-end memory networks have shown promising results on Question Answering task, which encode the past facts into an explicit memory and perform reasoning ability by making multiple computational steps on the memory. However, memory networks conduct the reasoning on sentence-level memory to output coarse semantic vectors and do not further take any attention mechanism to focus on words, which may lead to the model lose some detail information, especially when the answers are rare or unknown words. In this paper, we propose a novel Hierarchical Memory Networks, dubbed HMN. First, we encode the past facts into sentence-level memory and word-level memory respectively. Then, k-max pooling is exploited following reasoning module on the sentence-level memory to sample the k most relevant sentences to a question and feed these sentences into attention mechanism on the word-level memory to focus the words in the selected sentences. Finally, the prediction is jointly learned over the outputs of the sentence-level reasoning module and the word-level attention mechanism. The experimental results demonstrate that our approach successfully conducts answer selection on unknown words and achieves a better performance than memory networks.

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Hierarchical Memory Networks for Answer Selection on Unknown Words
Jiaming Xu | Jing Shi | Yiqun Yao | Suncong Zheng | Bo Xu | Bo Xu
Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers

Recently, end-to-end memory networks have shown promising results on Question Answering task, which encode the past facts into an explicit memory and perform reasoning ability by making multiple computational steps on the memory. However, memory networks conduct the reasoning on sentence-level memory to output coarse semantic vectors and do not further take any attention mechanism to focus on words, which may lead to the model lose some detail information, especially when the answers are rare or unknown words. In this paper, we propose a novel Hierarchical Memory Networks, dubbed HMN. First, we encode the past facts into sentence-level memory and word-level memory respectively. Then, k-max pooling is exploited following reasoning module on the sentence-level memory to sample the k most relevant sentences to a question and feed these sentences into attention mechanism on the word-level memory to focus the words in the selected sentences. Finally, the prediction is jointly learned over the outputs of the sentence-level reasoning module and the word-level attention mechanism. The experimental results demonstrate that our approach successfully conducts answer selection on unknown words and achieves a better performance than memory networks.

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A Character-Aware Encoder for Neural Machine Translation
Zhen Yang | Wei Chen | Feng Wang | Bo Xu
Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers

This article proposes a novel character-aware neural machine translation (NMT) model that views the input sequences as sequences of characters rather than words. On the use of row convolution (Amodei et al., 2015), the encoder of the proposed model composes word-level information from the input sequences of characters automatically. Since our model doesn’t rely on the boundaries between each word (as the whitespace boundaries in English), it is also applied to languages without explicit word segmentations (like Chinese). Experimental results on Chinese-English translation tasks show that the proposed character-aware NMT model can achieve comparable translation performance with the traditional word based NMT models. Despite the target side is still word based, the proposed model is able to generate much less unknown words.

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Text Classification Improved by Integrating Bidirectional LSTM with Two-dimensional Max Pooling
Peng Zhou | Zhenyu Qi | Suncong Zheng | Jiaming Xu | Hongyun Bao | Bo Xu
Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers

Recurrent Neural Network (RNN) is one of the most popular architectures used in Natural Language Processsing (NLP) tasks because its recurrent structure is very suitable to process variable-length text. RNN can utilize distributed representations of words by first converting the tokens comprising each text into vectors, which form a matrix. And this matrix includes two dimensions: the time-step dimension and the feature vector dimension. Then most existing models usually utilize one-dimensional (1D) max pooling operation or attention-based operation only on the time-step dimension to obtain a fixed-length vector. However, the features on the feature vector dimension are not mutually independent, and simply applying 1D pooling operation over the time-step dimension independently may destroy the structure of the feature representation. On the other hand, applying two-dimensional (2D) pooling operation over the two dimensions may sample more meaningful features for sequence modeling tasks. To integrate the features on both dimensions of the matrix, this paper explores applying 2D max pooling operation to obtain a fixed-length representation of the text. This paper also utilizes 2D convolution to sample more meaningful information of the matrix. Experiments are conducted on six text classification tasks, including sentiment analysis, question classification, subjectivity classification and newsgroup classification. Compared with the state-of-the-art models, the proposed models achieve excellent performance on 4 out of 6 tasks. Specifically, one of the proposed models achieves highest accuracy on Stanford Sentiment Treebank binary classification and fine-grained classification tasks.

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Attention-Based Bidirectional Long Short-Term Memory Networks for Relation Classification
Peng Zhou | Wei Shi | Jun Tian | Zhenyu Qi | Bingchen Li | Hongwei Hao | Bo Xu
Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

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Combining Lexical and Semantic-based Features for Answer Sentence Selection
Jing Shi | Jiaming Xu | Yiqun Yao | Suncong Zheng | Bo Xu
Proceedings of the Open Knowledge Base and Question Answering Workshop (OKBQA 2016)

Question answering is always an attractive and challenging task in natural language processing area. There are some open domain question answering systems, such as IBM Waston, which take the unstructured text data as input, in some ways of humanlike thinking process and a mode of artificial intelligence. At the conference on Natural Language Processing and Chinese Computing (NLPCC) 2016, China Computer Federation hosted a shared task evaluation about Open Domain Question Answering. We achieve the 2nd place at the document-based subtask. In this paper, we present our solution, which consists of feature engineering in lexical and semantic aspects and model training methods. As the result of the evaluation shows, our solution provides a valuable and brief model which could be used in modelling question answering or sentence semantic relevance. We hope our solution would contribute to this vast and significant task with some heuristic thinking.

2015

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Semi-supervised Chinese Word Segmentation based on Bilingual Information
Wei Chen | Bo Xu
Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing

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Short Text Clustering via Convolutional Neural Networks
Jiaming Xu | Peng Wang | Guanhua Tian | Bo Xu | Jun Zhao | Fangyuan Wang | Hongwei Hao
Proceedings of the 1st Workshop on Vector Space Modeling for Natural Language Processing

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Dialogue Management based on Multi-domain Corpus
Wendong Ge | Bo Xu
Proceedings of the 16th Annual Meeting of the Special Interest Group on Discourse and Dialogue

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Semantic Clustering and Convolutional Neural Network for Short Text Categorization
Peng Wang | Jiaming Xu | Bo Xu | Chenglin Liu | Heng Zhang | Fangyuan Wang | Hongwei Hao
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)

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Dialogue Management based on Sentence Clustering
Wendong Ge | Bo Xu
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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Learning New Semi-Supervised Deep Auto-encoder Features for Statistical Machine Translation
Shixiang Lu | Zhenbiao Chen | Bo Xu
Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

2013

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Phrase-based Parallel Fragments Extraction from Comparable Corpora
Xiaoyin Fu | Wei Wei | Shixiang Lu | Zhenbiao Chen | Bo Xu
Proceedings of the Sixth International Joint Conference on Natural Language Processing

2012

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Automated Essay Scoring Based on Finite State Transducer: towards ASR Transcription of Oral English Speech
Xingyuan Peng | Dengfeng Ke | Bo Xu
Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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Translation Model Based Cross-Lingual Language Model Adaptation: from Word Models to Phrase Models
Shixiang Lu | Wei Wei | Xiaoyin Fu | Bo Xu
Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning

2007

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Probabilistic Parsing Action Models for Multi-Lingual Dependency Parsing
Xiangyu Duan | Jun Zhao | Bo Xu
Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CoNLL)

2006

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Cluster-Based Language Model for Sentence Retrieval in Chinese Question Answering
Youzheng Wu | Jun Zhao | Bo Xu
Proceedings of the Fifth SIGHAN Workshop on Chinese Language Processing

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Robust Target Speaker Tracking in Broadcast TV Streams
Junmei Bai | Hongchen Jiang | Shilei Zhang | Shuwu Zhang | Bo Xu
International Journal of Computational Linguistics & Chinese Language Processing, Volume 11, Number 1, March 2006: Special Issue on Human Computer Speech Processing

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A Fast Framework for the Constrained Mean Trajectory Segment Model by Avoidance of Redundant Computation on Segment
Yun Tang | Wenju Liu | Yiyan Zhang | Bo Xu
International Journal of Computational Linguistics & Chinese Language Processing, Volume 11, Number 1, March 2006: Special Issue on Human Computer Speech Processing

2005

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Product Named Entity Recognition Based on Hierarchical Hidden Markov Model
Feifan Liu | Jun Zhao | Bibo Lv | Bo Xu | Hao Yu
Proceedings of the Fourth SIGHAN Workshop on Chinese Language Processing

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Chinese Named Entity Recognition with Multiple Features
Youzheng Wu | Jun Zhao | Bo Xu | Hao Yu
Proceedings of Human Language Technology Conference and Conference on Empirical Methods in Natural Language Processing

2003

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Chinese Named Entity Recognition Combining Statistical Model wih Human Knowledge
Youzheng Wu | Jun Zhao | Bo Xu
Proceedings of the ACL 2003 Workshop on Multilingual and Mixed-language Named Entity Recognition

2002

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Bridging the Gap between Dialogue management and dialogue models
Weiqun Xu | Bo Xu | Taiyi Huang | Hairong Xia
Proceedings of the Third SIGdial Workshop on Discourse and Dialogue

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Interactive Chinese-to-English Speech Translation Based on Dialogue Management
Chengqing Zong | Bo Xu | Taiyi Huang
Proceedings of the ACL-02 Workshop on Speech-to-Speech Translation: Algorithms and Systems

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Chinese Syntactic Parsing Based on Extended GLR Parsing Algorithm with PCFG*
Yan Zhang | Bo Xu | Chengqing Zong
COLING 2002: The 17th International Conference on Computational Linguistics: Project Notes