Zhongqiang Huang


2021

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MuVER: Improving First-Stage Entity Retrieval with Multi-View Entity Representations
Xinyin Ma | Yong Jiang | Nguyen Bach | Tao Wang | Zhongqiang Huang | Fei Huang | Weiming Lu
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

Entity retrieval, which aims at disambiguating mentions to canonical entities from massive KBs, is essential for many tasks in natural language processing. Recent progress in entity retrieval shows that the dual-encoder structure is a powerful and efficient framework to nominate candidates if entities are only identified by descriptions. However, they ignore the property that meanings of entity mentions diverge in different contexts and are related to various portions of descriptions, which are treated equally in previous works. In this work, we propose Multi-View Entity Representations (MuVER), a novel approach for entity retrieval that constructs multi-view representations for entity descriptions and approximates the optimal view for mentions via a heuristic searching method. Our method achieves the state-of-the-art performance on ZESHEL and improves the quality of candidates on three standard Entity Linking datasets.

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Word Reordering for Zero-shot Cross-lingual Structured Prediction
Tao Ji | Yong Jiang | Tao Wang | Zhongqiang Huang | Fei Huang | Yuanbin Wu | Xiaoling Wang
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

Adapting word order from one language to another is a key problem in cross-lingual structured prediction. Current sentence encoders (e.g., RNN, Transformer with position embeddings) are usually word order sensitive. Even with uniform word form representations (MUSE, mBERT), word order discrepancies may hurt the adaptation of models. In this paper, we build structured prediction models with bag-of-words inputs, and introduce a new reordering module to organizing words following the source language order, which learns task-specific reordering strategies from a general-purpose order predictor model. Experiments on zero-shot cross-lingual dependency parsing, POS tagging, and morphological tagging show that our model can significantly improve target language performances, especially for languages that are distant from the source language.

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A Unified Encoding of Structures in Transition Systems
Tao Ji | Yong Jiang | Tao Wang | Zhongqiang Huang | Fei Huang | Yuanbin Wu | Xiaoling Wang
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

Transition systems usually contain various dynamic structures (e.g., stacks, buffers). An ideal transition-based model should encode these structures completely and efficiently. Previous works relying on templates or neural network structures either only encode partial structure information or suffer from computation efficiency. In this paper, we propose a novel attention-based encoder unifying representation of all structures in a transition system. Specifically, we separate two views of items on structures, namely structure-invariant view and structure-dependent view. With the help of parallel-friendly attention network, we are able to encoding transition states with O(1) additional complexity (with respect to basic feature extractors). Experiments on the PTB and UD show that our proposed method significantly improves the test speed and achieves the best transition-based model, and is comparable to state-of-the-art methods.

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Manifold Adversarial Augmentation for Neural Machine Translation
Guandan Chen | Kai Fan | Kaibo Zhang | Boxing Chen | Zhongqiang Huang
Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021

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Structural Knowledge Distillation: Tractably Distilling Information for Structured Predictor
Xinyu Wang | Yong Jiang | Zhaohui Yan | Zixia Jia | Nguyen Bach | Tao Wang | Zhongqiang Huang | Fei Huang | Kewei Tu
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

Knowledge distillation is a critical technique to transfer knowledge between models, typically from a large model (the teacher) to a more fine-grained one (the student). The objective function of knowledge distillation is typically the cross-entropy between the teacher and the student’s output distributions. However, for structured prediction problems, the output space is exponential in size; therefore, the cross-entropy objective becomes intractable to compute and optimize directly. In this paper, we derive a factorized form of the knowledge distillation objective for structured prediction, which is tractable for many typical choices of the teacher and student models. In particular, we show the tractability and empirical effectiveness of structural knowledge distillation between sequence labeling and dependency parsing models under four different scenarios: 1) the teacher and student share the same factorization form of the output structure scoring function; 2) the student factorization produces more fine-grained substructures than the teacher factorization; 3) the teacher factorization produces more fine-grained substructures than the student factorization; 4) the factorization forms from the teacher and the student are incompatible.

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Improving Named Entity Recognition by External Context Retrieving and Cooperative Learning
Xinyu Wang | Yong Jiang | Nguyen Bach | Tao Wang | Zhongqiang Huang | Fei Huang | Kewei Tu
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

Recent advances in Named Entity Recognition (NER) show that document-level contexts can significantly improve model performance. In many application scenarios, however, such contexts are not available. In this paper, we propose to find external contexts of a sentence by retrieving and selecting a set of semantically relevant texts through a search engine, with the original sentence as the query. We find empirically that the contextual representations computed on the retrieval-based input view, constructed through the concatenation of a sentence and its external contexts, can achieve significantly improved performance compared to the original input view based only on the sentence. Furthermore, we can improve the model performance of both input views by Cooperative Learning, a training method that encourages the two input views to produce similar contextual representations or output label distributions. Experiments show that our approach can achieve new state-of-the-art performance on 8 NER data sets across 5 domains.

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Automated Concatenation of Embeddings for Structured Prediction
Xinyu Wang | Yong Jiang | Nguyen Bach | Tao Wang | Zhongqiang Huang | Fei Huang | Kewei Tu
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

Pretrained contextualized embeddings are powerful word representations for structured prediction tasks. Recent work found that better word representations can be obtained by concatenating different types of embeddings. However, the selection of embeddings to form the best concatenated representation usually varies depending on the task and the collection of candidate embeddings, and the ever-increasing number of embedding types makes it a more difficult problem. In this paper, we propose Automated Concatenation of Embeddings (ACE) to automate the process of finding better concatenations of embeddings for structured prediction tasks, based on a formulation inspired by recent progress on neural architecture search. Specifically, a controller alternately samples a concatenation of embeddings, according to its current belief of the effectiveness of individual embedding types in consideration for a task, and updates the belief based on a reward. We follow strategies in reinforcement learning to optimize the parameters of the controller and compute the reward based on the accuracy of a task model, which is fed with the sampled concatenation as input and trained on a task dataset. Empirical results on 6 tasks and 21 datasets show that our approach outperforms strong baselines and achieves state-of-the-art performance with fine-tuned embeddings in all the evaluations.

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Multi-View Cross-Lingual Structured Prediction with Minimum Supervision
Zechuan Hu | Yong Jiang | Nguyen Bach | Tao Wang | Zhongqiang Huang | Fei Huang | Kewei Tu
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

In structured prediction problems, cross-lingual transfer learning is an efficient way to train quality models for low-resource languages, and further improvement can be obtained by learning from multiple source languages. However, not all source models are created equal and some may hurt performance on the target language. Previous work has explored the similarity between source and target sentences as an approximate measure of strength for different source models. In this paper, we propose a multi-view framework, by leveraging a small number of labeled target sentences, to effectively combine multiple source models into an aggregated source view at different granularity levels (language, sentence, or sub-structure), and transfer it to a target view based on a task-specific model. By encouraging the two views to interact with each other, our framework can dynamically adjust the confidence level of each source model and improve the performance of both views during training. Experiments for three structured prediction tasks on sixteen data sets show that our framework achieves significant improvement over all existing approaches, including these with access to additional source language data.

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Risk Minimization for Zero-shot Sequence Labeling
Zechuan Hu | Yong Jiang | Nguyen Bach | Tao Wang | Zhongqiang Huang | Fei Huang | Kewei Tu
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

Zero-shot sequence labeling aims to build a sequence labeler without human-annotated datasets. One straightforward approach is utilizing existing systems (source models) to generate pseudo-labeled datasets and train a target sequence labeler accordingly. However, due to the gap between the source and the target languages/domains, this approach may fail to recover the true labels. In this paper, we propose a novel unified framework for zero-shot sequence labeling with minimum risk training and design a new decomposable risk function that models the relations between the predicted labels from the source models and the true labels. By making the risk function trainable, we draw a connection between minimum risk training and latent variable model learning. We propose a unified learning algorithm based on the expectation maximization (EM) algorithm. We extensively evaluate our proposed approaches on cross-lingual/domain sequence labeling tasks over twenty-one datasets. The results show that our approaches outperform state-of-the-art baseline systems.

2020

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AIN: Fast and Accurate Sequence Labeling with Approximate Inference Network
Xinyu Wang | Yong Jiang | Nguyen Bach | Tao Wang | Zhongqiang Huang | Fei Huang | Kewei Tu
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

The linear-chain Conditional Random Field (CRF) model is one of the most widely-used neural sequence labeling approaches. Exact probabilistic inference algorithms such as the forward-backward and Viterbi algorithms are typically applied in training and prediction stages of the CRF model. However, these algorithms require sequential computation that makes parallelization impossible. In this paper, we propose to employ a parallelizable approximate variational inference algorithm for the CRF model. Based on this algorithm, we design an approximate inference network that can be connected with the encoder of the neural CRF model to form an end-to-end network, which is amenable to parallelization for faster training and prediction. The empirical results show that our proposed approaches achieve a 12.7-fold improvement in decoding speed with long sentences and a competitive accuracy compared with the traditional CRF approach.

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An Investigation of Potential Function Designs for Neural CRF
Zechuan Hu | Yong Jiang | Nguyen Bach | Tao Wang | Zhongqiang Huang | Fei Huang | Kewei Tu
Findings of the Association for Computational Linguistics: EMNLP 2020

The neural linear-chain CRF model is one of the most widely-used approach to sequence labeling. In this paper, we investigate a series of increasingly expressive potential functions for neural CRF models, which not only integrate the emission and transition functions, but also explicitly take the representations of the contextual words as input. Our extensive experiments show that the decomposed quadrilinear potential function based on the vector representations of two neighboring labels and two neighboring words consistently achieves the best performance.

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More Embeddings, Better Sequence Labelers?
Xinyu Wang | Yong Jiang | Nguyen Bach | Tao Wang | Zhongqiang Huang | Fei Huang | Kewei Tu
Findings of the Association for Computational Linguistics: EMNLP 2020

Recent work proposes a family of contextual embeddings that significantly improves the accuracy of sequence labelers over non-contextual embeddings. However, there is no definite conclusion on whether we can build better sequence labelers by combining different kinds of embeddings in various settings. In this paper, we conduct extensive experiments on 3 tasks over 18 datasets and 8 languages to study the accuracy of sequence labeling with various embedding concatenations and make three observations: (1) concatenating more embedding variants leads to better accuracy in rich-resource and cross-domain settings and some conditions of low-resource settings; (2) concatenating contextual sub-word embeddings with contextual character embeddings hurts the accuracy in extremely low-resource settings; (3) based on the conclusion of (1), concatenating additional similar contextual embeddings cannot lead to further improvements. We hope these conclusions can help people build stronger sequence labelers in various settings.

2019

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Weakly Supervised Attentional Model for Low Resource Ad-hoc Cross-lingual Information Retrieval
Lingjun Zhao | Rabih Zbib | Zhuolin Jiang | Damianos Karakos | Zhongqiang Huang
Proceedings of the 2nd Workshop on Deep Learning Approaches for Low-Resource NLP (DeepLo 2019)

We propose a weakly supervised neural model for Ad-hoc Cross-lingual Information Retrieval (CLIR) from low-resource languages. Low resource languages often lack relevance annotations for CLIR, and when available the training data usually has limited coverage for possible queries. In this paper, we design a model which does not require relevance annotations, instead it is trained on samples extracted from translation corpora as weak supervision. This model relies on an attention mechanism to learn spans in the foreign sentence that are relevant to the query. We report experiments on two low resource languages: Swahili and Tagalog, trained on less that 100k parallel sentences each. The proposed model achieves 19 MAP points improvement compared to using CNNs for feature extraction, 12 points improvement from machine translation-based CLIR, and up to 6 points improvement compared to probabilistic CLIR models.

2015

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Statistical Machine Translation Features with Multitask Tensor Networks
Hendra Setiawan | Zhongqiang Huang | Jacob Devlin | Thomas Lamar | Rabih Zbib | Richard Schwartz | John Makhoul
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)

2014

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Fast and Robust Neural Network Joint Models for Statistical Machine Translation
Jacob Devlin | Rabih Zbib | Zhongqiang Huang | Thomas Lamar | Richard Schwartz | John Makhoul
Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

2013

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Factored Soft Source Syntactic Constraints for Hierarchical Machine Translation
Zhongqiang Huang | Jacob Devlin | Rabih Zbib
Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing

2011

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Feature-Rich Log-Linear Lexical Model for Latent Variable PCFG Grammars
Zhongqiang Huang | Mary Harper
Proceedings of 5th International Joint Conference on Natural Language Processing

2010

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Appropriately Handled Prosodic Breaks Help PCFG Parsing
Zhongqiang Huang | Mary Harper
Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics

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Self-Training with Products of Latent Variable Grammars
Zhongqiang Huang | Mary Harper | Slav Petrov
Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing

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Soft Syntactic Constraints for Hierarchical Phrase-Based Translation Using Latent Syntactic Distributions
Zhongqiang Huang | Martin Čmejrek | Bowen Zhou
Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing

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Lessons Learned in Part-of-Speech Tagging of Conversational Speech
Vladimir Eidelman | Zhongqiang Huang | Mary Harper
Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing

2009

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Improving A Simple Bigram HMM Part-of-Speech Tagger by Latent Annotation and Self-Training
Zhongqiang Huang | Vladimir Eidelman | Mary Harper
Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics, Companion Volume: Short Papers

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Self-Training PCFG Grammars with Latent Annotations Across Languages
Zhongqiang Huang | Mary Harper
Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing

2007

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Mandarin Part-of-Speech Tagging and Discriminative Reranking
Zhongqiang Huang | Mary Harper | Wen Wang
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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An Open Source Prosodic Feature Extraction Tool
Zhongqiang Huang | Lei Chen | Mary Harper
Proceedings of the Fifth International Conference on Language Resources and Evaluation (LREC’06)

There has been an increasing interest in utilizing a wide variety of knowledge sources in order to perform automatic tagging of speech events, such as sentence boundaries and dialogue acts. In addition to the word spoken, the prosodic content of the speech has been proved quite valuable in a variety of spoken language processing tasks such as sentence segmentation and tagging, disfluency detection, dialog act segmentation and tagging, and speaker recognition. In this paper, we report on an open source prosodic feature extraction tool based on Praat, with a description of the prosodic features and the implementation details, as well as a discussion of its extension capability. We also evaluate our tool on a sentence boundary detection task and report the system performance on the NIST RT04 CTS data.