Zhengdong Lu


2019

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A Prism Module for Semantic Disentanglement in Name Entity Recognition
Kun Liu | Shen Li | Daqi Zheng | Zhengdong Lu | Sheng Gao | Si Li
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

Natural Language Processing has been perplexed for many years by the problem that multiple semantics are mixed inside a word, even with the help of context. To solve this problem, we propose a prism module to disentangle the semantic aspects of words and reduce noise at the input layer of a model. In the prism module, some words are selectively replaced with task-related semantic aspects, then these denoised word representations can be fed into downstream tasks to make them easier. Besides, we also introduce a structure to train this module jointly with the downstream model without additional data. This module can be easily integrated into the downstream model and significantly improve the performance of baselines on named entity recognition (NER) task. The ablation analysis demonstrates the rationality of the method. As a side effect, the proposed method also provides a way to visualize the contribution of each word.

2018

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Object-oriented Neural Programming (OONP) for Document Understanding
Zhengdong Lu | Xianggen Liu | Haotian Cui | Yukun Yan | Daqi Zheng
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

We propose Object-oriented Neural Programming (OONP), a framework for semantically parsing documents in specific domains. Basically, OONP reads a document and parses it into a predesigned object-oriented data structure that reflects the domain-specific semantics of the document. An OONP parser models semantic parsing as a decision process: a neural net-based Reader sequentially goes through the document, and builds and updates an intermediate ontology during the process to summarize its partial understanding of the text. OONP supports a big variety of forms (both symbolic and differentiable) for representing the state and the document, and a rich family of operations to compose the representation. An OONP parser can be trained with supervision of different forms and strength, including supervised learning (SL), reinforcement learning (RL) and hybrid of the two. Our experiments on both synthetic and real-world document parsing tasks have shown that OONP can learn to handle fairly complicated ontology with training data of modest sizes.

2017

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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 Neural Machine Translation with Linear Associative Unit
Mingxuan Wang | Zhengdong Lu | Jie Zhou | Qun Liu
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Deep Neural Networks (DNNs) have provably enhanced the state-of-the-art Neural Machine Translation (NMT) with its capability in modeling complex functions and capturing complex linguistic structures. However NMT with deep architecture in its encoder or decoder RNNs often suffer from severe gradient diffusion due to the non-linear recurrent activations, which often makes the optimization much more difficult. To address this problem we propose a novel linear associative units (LAU) to reduce the gradient propagation path inside the recurrent unit. Different from conventional approaches (LSTM unit and GRU), LAUs uses linear associative connections between input and output of the recurrent unit, which allows unimpeded information flow through both space and time The model is quite simple, but it is surprisingly effective. Our empirical study on Chinese-English translation shows that our model with proper configuration can improve by 11.7 BLEU upon Groundhog and the best reported on results in the same setting. On WMT14 English-German task and a larger WMT14 English-French task, our model achieves comparable results with the state-of-the-art.

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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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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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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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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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