Muhua Zhu


2020

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Coupling Distant Annotation and Adversarial Training for Cross-Domain Chinese Word Segmentation
Ning Ding | Dingkun Long | Guangwei Xu | Muhua Zhu | Pengjun Xie | Xiaobin Wang | Haitao Zheng
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

Fully supervised neural approaches have achieved significant progress in the task of Chinese word segmentation (CWS). Nevertheless, the performance of supervised models always drops gravely if the domain shifts due to the distribution gap across domains and the out of vocabulary (OOV) problem. In order to simultaneously alleviate the issues, this paper intuitively couples distant annotation and adversarial training for cross-domain CWS. 1) We rethink the essence of “Chinese words” and design an automatic distant annotation mechanism, which does not need any supervision or pre-defined dictionaries on the target domain. The method could effectively explore domain-specific words and distantly annotate the raw texts for the target domain. 2) We further develop a sentence-level adversarial training procedure to perform noise reduction and maximum utilization of the source domain information. Experiments on multiple real-world datasets across various domains show the superiority and robustness of our model, significantly outperforming previous state-of-the-arts cross-domain CWS methods.

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Improving AMR Parsing with Sequence-to-Sequence Pre-training
Dongqin Xu | Junhui Li | Muhua Zhu | Min Zhang | Guodong Zhou
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

In the literature, the research on abstract meaning representation (AMR) parsing is much restricted by the size of human-curated dataset which is critical to build an AMR parser with good performance. To alleviate such data size restriction, pre-trained models have been drawing more and more attention in AMR parsing. However, previous pre-trained models, like BERT, are implemented for general purpose which may not work as expected for the specific task of AMR parsing. In this paper, we focus on sequence-to-sequence (seq2seq) AMR parsing and propose a seq2seq pre-training approach to build pre-trained models in both single and joint way on three relevant tasks, i.e., machine translation, syntactic parsing, and AMR parsing itself. Moreover, we extend the vanilla fine-tuning method to a multi-task learning fine-tuning method that optimizes for the performance of AMR parsing while endeavors to preserve the response of pre-trained models. Extensive experimental results on two English benchmark datasets show that both the single and joint pre-trained models significantly improve the performance (e.g., from 71.5 to 80.2 on AMR 2.0), which reaches the state of the art. The result is very encouraging since we achieve this with seq2seq models rather than complex models. We make our code and model available at https:// github.com/xdqkid/S2S-AMR-Parser.

2019

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Modeling Graph Structure in Transformer for Better AMR-to-Text Generation
Jie Zhu | Junhui Li | Muhua Zhu | Longhua Qian | Min Zhang | Guodong Zhou
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

Recent studies on AMR-to-text generation often formalize the task as a sequence-to-sequence (seq2seq) learning problem by converting an Abstract Meaning Representation (AMR) graph into a word sequences. Graph structures are further modeled into the seq2seq framework in order to utilize the structural information in the AMR graphs. However, previous approaches only consider the relations between directly connected concepts while ignoring the rich structure in AMR graphs. In this paper we eliminate such a strong limitation and propose a novel structure-aware self-attention approach to better model the relations between indirectly connected concepts in the state-of-the-art seq2seq model, i.e. the Transformer. In particular, a few different methods are explored to learn structural representations between two concepts. Experimental results on English AMR benchmark datasets show that our approach significantly outperforms the state-of-the-art with 29.66 and 31.82 BLEU scores on LDC2015E86 and LDC2017T10, respectively. To the best of our knowledge, these are the best results achieved so far by supervised models on the benchmarks.

2017

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Modeling Source Syntax for Neural Machine Translation
Junhui Li | Deyi Xiong | Zhaopeng Tu | Muhua Zhu | Min Zhang | Guodong Zhou
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Even though a linguistics-free sequence to sequence model in neural machine translation (NMT) has certain capability of implicitly learning syntactic information of source sentences, this paper shows that source syntax can be explicitly incorporated into NMT effectively to provide further improvements. Specifically, we linearize parse trees of source sentences to obtain structural label sequences. On the basis, we propose three different sorts of encoders to incorporate source syntax into NMT: 1) Parallel RNN encoder that learns word and label annotation vectors parallelly; 2) Hierarchical RNN encoder that learns word and label annotation vectors in a two-level hierarchy; and 3) Mixed RNN encoder that stitchingly learns word and label annotation vectors over sequences where words and labels are mixed. Experimentation on Chinese-to-English translation demonstrates that all the three proposed syntactic encoders are able to improve translation accuracy. It is interesting to note that the simplest RNN encoder, i.e., Mixed RNN encoder yields the best performance with an significant improvement of 1.4 BLEU points. Moreover, an in-depth analysis from several perspectives is provided to reveal how source syntax benefits NMT.

2016

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SoNLP-DP System for ConLL-2016 English Shallow Discourse Parsing
Fang Kong | Sheng Li | Junhui Li | Muhua Zhu | Guodong Zhou
Proceedings of the CoNLL-16 shared task

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SoNLP-DP System for ConLL-2016 Chinese Shallow Discourse Parsing
Junhui Li | Fang Kong | Sheng Li | Muhua Zhu | Guodong Zhou
Proceedings of the CoNLL-16 shared task

2015

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Improving Semantic Parsing with Enriched Synchronous Context-Free Grammar
Junhui Li | Muhua Zhu | Wei Lu | Guodong Zhou
Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing

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NiuParser: A Chinese Syntactic and Semantic Parsing Toolkit
Jingbo Zhu | Muhua Zhu | Qiang Wang | Tong Xiao
Proceedings of ACL-IJCNLP 2015 System Demonstrations

2013

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Fast and Accurate Shift-Reduce Constituent Parsing
Muhua Zhu | Yue Zhang | Wenliang Chen | Min Zhang | Jingbo Zhu
Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

2012

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Exploiting Lexical Dependencies from Large-Scale Data for Better Shift-Reduce Constituency Parsing
Muhua Zhu | Jingbo Zhu | Huizhen Wang
Proceedings of COLING 2012

2011

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Better Automatic Treebank Conversion Using A Feature-Based Approach
Muhua Zhu | Jingbo Zhu | Minghan Hu
Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies

2010

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High OOV-Recall Chinese Word Segmenter
Xiaoming Xu | Muhua Zhu | Xiaoxu Fei | Jingbo Zhu
CIPS-SIGHAN Joint Conference on Chinese Language Processing

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Heterogeneous Parsing via Collaborative Decoding
Muhua Zhu | Jingbo Zhu | Tong Xiao
Proceedings of the 23rd International Conference on Computational Linguistics (Coling 2010)

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An Empirical Study of Translation Rule Extraction with Multiple Parsers
Tong Xiao | Jingbo Zhu | Hao Zhang | Muhua Zhu
Coling 2010: Posters

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Automatic Treebank Conversion via Informed Decoding
Muhua Zhu | Jingbo Zhu
Coling 2010: Posters

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Boosting-Based System Combination for Machine Translation
Tong Xiao | Jingbo Zhu | Muhua Zhu | Huizhen Wang
Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics

2009

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Chinese-English Organization Name Translation Based on Correlative Expansion
Feiliang Ren | Muhua Zhu | Huizhen Wang | Jingbo Zhu
Proceedings of the 2009 Named Entities Workshop: Shared Task on Transliteration (NEWS 2009)

2006

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Exploring Distributional Similarity Based Models for Query Spelling Correction
Mu Li | Muhua Zhu | Yang Zhang | Ming Zhou
Proceedings of the 21st International Conference on Computational Linguistics and 44th Annual Meeting of the Association for Computational Linguistics

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Designing Special Post-Processing Rules for SVM-Based Chinese Word Segmentation
Muhua Zhu | Yilin Wang | Zhenxing Wang | Huizhen Wang | Jingbo Zhu
Proceedings of the Fifth SIGHAN Workshop on Chinese Language Processing