Hang Li


2020

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Fact-based Text Editing
Hayate Iso | Chao Qiao | Hang Li
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

We propose a novel text editing task, referred to as fact-based text editing, in which the goal is to revise a given document to better describe the facts in a knowledge base (e.g., several triples). The task is important in practice because reflecting the truth is a common requirement in text editing. First, we propose a method for automatically generating a dataset for research on fact-based text editing, where each instance consists of a draft text, a revised text, and several facts represented in triples. We apply the method into two public table-to-text datasets, obtaining two new datasets consisting of 233k and 37k instances, respectively. Next, we propose a new neural network architecture for fact-based text editing, called FactEditor, which edits a draft text by referring to given facts using a buffer, a stream, and a memory. A straightforward approach to address the problem would be to employ an encoder-decoder model. Our experimental results on the two datasets show that FactEditor outperforms the encoder-decoder approach in terms of fidelity and fluency. The results also show that FactEditor conducts inference faster than the encoder-decoder approach.

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Spelling Error Correction with Soft-Masked BERT
Shaohua Zhang | Haoran Huang | Jicong Liu | Hang Li
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

Spelling error correction is an important yet challenging task because a satisfactory solution of it essentially needs human-level language understanding ability. Without loss of generality we consider Chinese spelling error correction (CSC) in this paper. A state-of-the-art method for the task selects a character from a list of candidates for correction (including non-correction) at each position of the sentence on the basis of BERT, the language representation model. The accuracy of the method can be sub-optimal, however, because BERT does not have sufficient capability to detect whether there is an error at each position, apparently due to the way of pre-training it using mask language modeling. In this work, we propose a novel neural architecture to address the aforementioned issue, which consists of a network for error detection and a network for error correction based on BERT, with the former being connected to the latter with what we call soft-masking technique. Our method of using ‘Soft-Masked BERT’ is general, and it may be employed in other language detection-correction problems. Experimental results on two datasets, including one large dataset which we create and plan to release, demonstrate that the performance of our proposed method is significantly better than the baselines including the one solely based on BERT.

2018

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Paraphrase Generation with Deep Reinforcement Learning
Zichao Li | Xin Jiang | Lifeng Shang | Hang Li
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

Automatic generation of paraphrases from a given sentence is an important yet challenging task in natural language processing (NLP). In this paper, we present a deep reinforcement learning approach to paraphrase generation. Specifically, we propose a new framework for the task, which consists of a generator and an evaluator, both of which are learned from data. The generator, built as a sequence-to-sequence learning model, can produce paraphrases given a sentence. The evaluator, constructed as a deep matching model, can judge whether two sentences are paraphrases of each other. The generator is first trained by deep learning and then further fine-tuned by reinforcement learning in which the reward is given by the evaluator. For the learning of the evaluator, we propose two methods based on supervised learning and inverse reinforcement learning respectively, depending on the type of available training data. Experimental results on two datasets demonstrate the proposed models (the generators) can produce more accurate paraphrases and outperform the state-of-the-art methods in paraphrase generation in both automatic evaluation and human evaluation.

2017

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Chunk-Based Bi-Scale Decoder for Neural Machine Translation
Hao Zhou | Zhaopeng Tu | Shujian Huang | Xiaohua Liu | Hang Li | Jiajun Chen
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

In typical neural machine translation (NMT), the decoder generates a sentence word by word, packing all linguistic granularities in the same time-scale of RNN. In this paper, we propose a new type of decoder for NMT, which splits the decode state into two parts and updates them in two different time-scales. Specifically, we first predict a chunk time-scale state for phrasal modeling, on top of which multiple word time-scale states are generated. In this way, the target sentence is translated hierarchically from chunks to words, with information in different granularities being leveraged. Experiments show that our proposed model significantly improves the translation performance over the state-of-the-art NMT model.

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Variation Autoencoder Based Network Representation Learning for Classification
Hang Li | Haozheng Wang | Zhenglu Yang | Masato Odagaki
Proceedings of ACL 2017, Student Research Workshop

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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.

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Deep Active Learning for Dialogue Generation
Nabiha Asghar | Pascal Poupart | Xin Jiang | Hang Li
Proceedings of the 6th Joint Conference on Lexical and Computational Semantics (*SEM 2017)

We propose an online, end-to-end, neural generative conversational model for open-domain dialogue. It is trained using a unique combination of offline two-phase supervised learning and online human-in-the-loop active learning. While most existing research proposes offline supervision or hand-crafted reward functions for online reinforcement, we devise a novel interactive learning mechanism based on hamming-diverse beam search for response generation and one-character user-feedback at each step. Experiments show that our model inherently promotes the generation of semantically relevant and interesting responses, and can be used to train agents with customized personas, moods and conversational styles.

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Cascaded Attention based Unsupervised Information Distillation for Compressive Summarization
Piji Li | Wai Lam | Lidong Bing | Weiwei Guo | Hang Li
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

When people recall and digest what they have read for writing summaries, the important content is more likely to attract their attention. Inspired by this observation, we propose a cascaded attention based unsupervised model to estimate the salience information from the text for compressive multi-document summarization. The attention weights are learned automatically by an unsupervised data reconstruction framework which can capture the sentence salience. By adding sparsity constraints on the number of output vectors, we can generate condensed information which can be treated as word salience. Fine-grained and coarse-grained sentence compression strategies are incorporated to produce compressive summaries. Experiments on some benchmark data sets show that our framework achieves better results than the state-of-the-art methods.

2016

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Memory-enhanced Decoder for Neural Machine Translation
Mingxuan Wang | Zhengdong Lu | Hang Li | Qun Liu
Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing

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A Novel Approach to Dropped Pronoun Translation
Longyue Wang | Zhaopeng Tu | Xiaojun Zhang | Hang Li | Andy Way | Qun Liu
Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

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Recent Progress in Deep Learning for NLP
Zhengdong Lu | Hang Li
Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Tutorial Abstracts

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Interactive Attention for Neural Machine Translation
Fandong Meng | Zhengdong Lu | Hang Li | Qun Liu
Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers

Conventional attention-based Neural Machine Translation (NMT) conducts dynamic alignment in generating the target sentence. By repeatedly reading the representation of source sentence, which keeps fixed after generated by the encoder (Bahdanau et al., 2015), the attention mechanism has greatly enhanced state-of-the-art NMT. In this paper, we propose a new attention mechanism, called INTERACTIVE ATTENTION, which models the interaction between the decoder and the representation of source sentence during translation by both reading and writing operations. INTERACTIVE ATTENTION can keep track of the interaction history and therefore improve the translation performance. Experiments on NIST Chinese-English translation task show that INTERACTIVE ATTENTION can achieve significant improvements over both the previous attention-based NMT baseline and some state-of-the-art variants of attention-based NMT (i.e., coverage models (Tu et al., 2016)). And neural machine translator with our INTERACTIVE ATTENTION can outperform the open source attention-based NMT system Groundhog by 4.22 BLEU points and the open source phrase-based system Moses by 3.94 BLEU points averagely on multiple test sets.

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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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Incorporating Copying Mechanism in Sequence-to-Sequence Learning
Jiatao Gu | Zhengdong Lu | Hang Li | Victor O.K. Li
Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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Neural Enquirer: Learning to Query Tables in Natural Language
Pengcheng Yin | Zhengdong Lu | Hang Li | Kao Ben
Proceedings of the Workshop on Human-Computer Question Answering

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Neural Generative Question Answering
Jun Yin | Xin Jiang | Zhengdong Lu | Lifeng Shang | Hang Li | Xiaoming Li
Proceedings of the Workshop on Human-Computer Question Answering

2015

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Encoding Source Language with Convolutional Neural Network for Machine Translation
Fandong Meng | Zhengdong Lu | Mingxuan Wang | Hang Li | Wenbin Jiang | Qun Liu
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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genCNN: A Convolutional Architecture for Word Sequence Prediction
Mingxuan Wang | Zhengdong Lu | Hang Li | Wenbin Jiang | Qun Liu
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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Neural Responding Machine for Short-Text Conversation
Lifeng Shang | Zhengdong Lu | Hang Li
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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Context-Dependent Translation Selection Using Convolutional Neural Network
Baotian Hu | Zhaopeng Tu | Zhengdong Lu | Hang Li | Qingcai Chen
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)

2013

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A Dataset for Research on Short-Text Conversations
Hao Wang | Zhengdong Lu | Hang Li | Enhong Chen
Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing

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The Companion Volume of the Proceedings of IJCNLP 2013: System Demonstrations
Kentaro Torisawa | Hang Li
The Companion Volume of the Proceedings of IJCNLP 2013: System Demonstrations

2012

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String Re-writing Kernel
Fan Bu | Hang Li | Xiaoyan Zhu
Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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Automatically Mining Question Reformulation Patterns from Search Log Data
Xiaobing Xue | Yu Tao | Daxin Jiang | Hang Li
Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

2011

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A Fast and Accurate Method for Approximate String Search
Ziqi Wang | Gu Xu | Hang Li | Ming Zhang
Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies

2010

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Query Understanding in Web Search - by Large Scale Log Data Mining and Statistical Learning
Hang Li
Proceedings of the Second Workshop on NLP Challenges in the Information Explosion Era (NLPIX 2010)

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Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing
Hang Li | Lluís Màrquez
Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing

2009

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Learning to Rank
Hang Li
Tutorial Abstracts of ACL-IJCNLP 2009

2008

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HTM: A Topic Model for Hypertexts
Congkai Sun | Bin Gao | Zhenfu Cao | Hang Li
Proceedings of the 2008 Conference on Empirical Methods in Natural Language Processing

2007

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A Unified Tagging Approach to Text Normalization
Conghui Zhu | Jie Tang | Hang Li | Hwee Tou Ng | Tiejun Zhao
Proceedings of the 45th Annual Meeting of the Association of Computational Linguistics

2004

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Word Translation Disambiguation Using Bilingual Bootstrapping
Hang Li | Cong Li
Computational Linguistics, Volume 30, Number 1, March 2004

2003

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Uncertainty Reduction in Collaborative Bootstrapping: Measure and Algorithm
Yunbo Cao | Hang Li | Li Lian
Proceedings of the 41st Annual Meeting of the Association for Computational Linguistics

2002

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Exploring Asymmetric Clustering for Statistical Language Modeling
Jianfeng Gao | Joshua Goodman | Guihong Cao | Hang Li
Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics

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Word Translation Disambiguation Using Bilingual Bootstrapping
Cong Li | Hang Li
Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics

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Base Noun Phrase Translation Using Web Data and the EM Algorithm
Yunbo Cao | Hang Li
COLING 2002: The 19th International Conference on Computational Linguistics

2000

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Topic Analysis Using a Finite Mixture Model
Hang Li | Kenji Yamanishi
2000 Joint SIGDAT Conference on Empirical Methods in Natural Language Processing and Very Large Corpora

1999

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Learning Dependencies between Case Frame Slots
Hang Li | Naoki Abe
Computational Linguistics, Volume 25, Number 2, June 1999

1998

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Word Clustering and Disambiguation Based on Co-occurrence Data
Hang Li | Naoki Abe
COLING 1998 Volume 2: The 17th International Conference on Computational Linguistics

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Word Clustering and Disambiguation Based on Co-occurrence Data
Hang Li | Naoki Abe
36th Annual Meeting of the Association for Computational Linguistics and 17th International Conference on Computational Linguistics, Volume 2

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Generalizing Case Frames Using a Thesaurus and the MDL Principle
Hang Li | Naoki Abe
Computational Linguistics, Volume 24, Number 2, June 1998

1997

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Document Classification Using a Finite Mixture Model
Hang Li | Kenji Yamanishi
35th Annual Meeting of the Association for Computational Linguistics and 8th Conference of the European Chapter of the Association for Computational Linguistics

1996

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Clustering Words with the MDL Principle
Hang Li | Naoki Abe
COLING 1996 Volume 1: The 16th International Conference on Computational Linguistics

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Learning Dependencies between Case Frame Slots
Hang Li | Naoki Abe
COLING 1996 Volume 1: The 16th International Conference on Computational Linguistics

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A Probabilistic Disambiguation Method Based on Psycholinguistic Principles
Hang Li
Fourth Workshop on Very Large Corpora