Hao Zhang


Translate-Train Embracing Translationese Artifacts
Sicheng Yu | Qianru Sun | Hao Zhang | Jing Jiang
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

Translate-train is a general training approach to multilingual tasks. The key idea is to use the translator of the target language to generate training data to mitigate the gap between the source and target languages. However, its performance is often hampered by the artifacts in the translated texts (translationese). We discover that such artifacts have common patterns in different languages and can be modeled by deep learning, and subsequently propose an approach to conduct translate-train using Translationese Embracing the effect of Artifacts (TEA). TEA learns to mitigate such effect on the training data of a source language (whose original and translationese are both available), and applies the learned module to facilitate the inference on the target language. Extensive experiments on the multilingual QA dataset TyDiQA demonstrate that TEA outperforms strong baselines.

FCGCL: Fine- and Coarse-Granularity Contrastive Learning for Speech Translation
Hao Zhang | Nianwen Si | Yaqi Chen | Zhen Li | Tong Niu | Xukui Yang | Dan Qu
Findings of the Association for Computational Linguistics: EMNLP 2022

It is notoriously difficult to implement end-to-end speech translation (E2E-ST) model because of the task complexity and data scarcity. Existing techniques often attempt to carry out implicit knowledge transfer from machine translation (MT) to ST model by imposing various constraints. However, in this transfer scenario, a significant problem is that the performance of the MT will drop significantly and the final transfer effect is also restricted. In this article, we recommend Fine and Coarse Granularity Contrastive Learning (FCGCL), which conduct explicit knowledge transfer from MT to ST model. Specially, we ensure through multi granularity contrastive learning that inputs with similar semantic between different modalities are encoded closely in the shared semantic space while inputs with different semantics are kept apart. Experiments on the MuST-C datasets on all 8 languages and further analysis show that our method can effectively improve the E2E-ST performance and achieves an average BLEU of 29.0.

UKP-SQuARE v2: Explainability and Adversarial Attacks for Trustworthy QA
Rachneet Sachdeva | Haritz Puerto | Tim Baumgärtner | Sewin Tariverdian | Hao Zhang | Kexin Wang | Hossain Shaikh Saadi | Leonardo F. R. Ribeiro | Iryna Gurevych
Proceedings of the 2nd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint Conference on Natural Language Processing: System Demonstrations

Question Answering (QA) systems are increasingly deployed in applications where they support real-world decisions. However, state-of-the-art models rely on deep neural networks, which are difficult to interpret by humans. Inherently interpretable models or post hoc explainability methods can help users to comprehend how a model arrives at its prediction and, if successful, increase their trust in the system. Furthermore, researchers can leverage these insights to develop new methods that are more accurate and less biased. In this paper, we introduce SQuARE v2, the new version of SQuARE, to provide an explainability infrastructure for comparing models based on methods such as saliency maps and graph-based explanations. While saliency maps are useful to inspect the importance of each input token for the model’s prediction, graph-based explanations from external Knowledge Graphs enable the users to verify the reasoning behind the model prediction. In addition, we provide multiple adversarial attacks to compare the robustness of QA models. With these explainability methods and adversarial attacks, we aim to ease the research on trustworthy QA models. SQuARE is available on https://square.ukp-lab.de.

Language Model Decomposition: Quantifying the Dependency and Correlation of Language Models
Hao Zhang
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

Pre-trained language models (LMs), such as BERT (Devlin et al., 2018) and its variants, have led to significant improvements on various NLP tasks in past years. However, a theoretical framework for studying their relationships is still missing. In this paper, we fill this gap by investigating the linear dependency between pre-trained LMs. The linear dependency of LMs is defined analogously to the linear dependency of vectors. We propose Language Model Decomposition (LMD) to represent a LM using a linear combination of other LMs as basis, and derive the closed-form solution. A goodness-of-fit metric for LMD similar to the coefficient of determination is defined and used to measure the linear dependency of a set of LMs. In experiments, we find that BERT and eleven (11) BERT-like LMs are 91% linearly dependent. This observation suggests that current state-of-the-art (SOTA) LMs are highly “correlated”. To further advance SOTA we need more diverse and novel LMs that are less dependent on existing LMs.

STGN: an Implicit Regularization Method for Learning with Noisy Labels in Natural Language Processing
Tingting Wu | Xiao Ding | Minji Tang | Hao Zhang | Bing Qin | Ting Liu
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

Noisy labels are ubiquitous in natural language processing (NLP) tasks. Existing work, namely learning with noisy labels in NLP, is often limited to dedicated tasks or specific training procedures, making it hard to be widely used. To address this issue, SGD noise has been explored to provide a more general way to alleviate the effect of noisy labels by involving benign noise in the process of stochastic gradient descent. However, previous studies exert identical perturbation for all samples, which may cause overfitting on incorrect ones or optimizing correct ones inadequately. To facilitate this, we propose a novel stochastic tailor-made gradient noise (STGN), mitigating the effect of inherent label noise by introducing tailor-made benign noise for each sample. Specifically, we investigate multiple principles to precisely and stably discriminate correct samples from incorrect ones and thus apply different intensities of perturbation to them. A detailed theoretical analysis shows that STGN has good properties, beneficial for model generalization. Experiments on three different NLP tasks demonstrate the effectiveness and versatility of STGN. Also, STGN can boost existing robust training methods.

Interventional Training for Out-Of-Distribution Natural Language Understanding
Sicheng Yu | Jing Jiang | Hao Zhang | Yulei Niu | Qianru Sun | Lidong Bing
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

Out-of-distribution (OOD) settings are used to measure a model’s performance when the distribution of the test data is different from that of the training data. NLU models are known to suffer in OOD. We study this issue from the perspective of causality, which sees confounding bias as the reason for models to learn spurious correlations. While a common solution is to perform intervention, existing methods handle only known and single confounder, but in many NLU tasks the confounders can be both unknown and multifactorial. In this paper, we propose a novel interventional training method called Bottom-up Automatic Intervention (BAI) that performs multi-granular intervention with identified multifactorial confounders. Our experiments on three NLU tasks, namely, natural language inference, fact verification and paraphrase identification, show the effectiveness of BAI for tackling OOD settings.

GUTS at SemEval-2022 Task 4: Adversarial Training and Balancing Methods for Patronizing and Condescending Language Detection
Junyu Lu | Hao Zhang | Tongyue Zhang | Hongbo Wang | Haohao Zhu | Bo Xu | Hongfei Lin
Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022)

Patronizing and Condescending Language (PCL) towards vulnerable communities in general media has been shown to have potentially harmful effects. Due to its subtlety and the good intentions behind its use, the audience is not aware of the language’s toxicity. In this paper, we present our method for the SemEval-2022 Task4 titled “Patronizing and Condescending Language Detection”. In Subtask A, a binary classification task, we introduce adversarial training based on Fast Gradient Method (FGM) and employ pre-trained model in a unified architecture. For Subtask B, framed as a multi-label classification problem, we utilize various improved multi-label cross-entropy loss functions and analyze the performance of our method. In the final evaluation, our system achieved official rankings of 17/79 and 16/49 on Subtask A and Subtask B, respectively. In addition, we explore the relationship between PCL and emotional polarity and intensity it contains.

Incorporating Instructional Prompts into a Unified Generative Framework for Joint Multiple Intent Detection and Slot Filling
Yangjun Wu | Han Wang | Dongxiang Zhang | Gang Chen | Hao Zhang
Proceedings of the 29th International Conference on Computational Linguistics

The joint multiple Intent Detection (ID) and Slot Filling (SF) is a significant challenge in spoken language understanding. Because the slots in an utterance may relate to multi-intents, most existing approaches focus on utilizing task-specific components to capture the relations between intents and slots. The customized networks restrict models from modeling commonalities between tasks and generalization for broader applications. To address the above issue, we propose a Unified Generative framework (UGEN) based on a prompt-based paradigm, and formulate the task as a question-answering problem. Specifically, we design 5-type templates as instructional prompts, and each template includes a question that acts as the driver to teach UGEN to grasp the paradigm, options that list the candidate intents or slots to reduce the answer search space, and the context denotes original utterance. Through the instructional prompts, UGEN is guided to understand intents, slots, and their implicit correlations. On two popular multi-intent benchmark datasets, experimental results demonstrate that UGEN achieves new SOTA performances on full-data and surpasses the baselines by a large margin on 5-shot (28.1%) and 10-shot (23%) scenarios, which verify that UGEN is robust and effective.


Parallel Attention Network with Sequence Matching for Video Grounding
Hao Zhang | Aixin Sun | Wei Jing | Liangli Zhen | Joey Tianyi Zhou | Siow Mong Rick Goh
Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021

COSY: COunterfactual SYntax for Cross-Lingual Understanding
Sicheng Yu | Hao Zhang | Yulei Niu | Qianru Sun | Jing Jiang
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)

Pre-trained multilingual language models, e.g., multilingual-BERT, are widely used in cross-lingual tasks, yielding the state-of-the-art performance. However, such models suffer from a large performance gap between source and target languages, especially in the zero-shot setting, where the models are fine-tuned only on English but tested on other languages for the same task. We tackle this issue by incorporating language-agnostic information, specifically, universal syntax such as dependency relations and POS tags, into language models, based on the observation that universal syntax is transferable across different languages. Our approach, called COunterfactual SYntax (COSY), includes the design of SYntax-aware networks as well as a COunterfactual training method to implicitly force the networks to learn not only the semantics but also the syntax. To evaluate COSY, we conduct cross-lingual experiments on natural language inference and question answering using mBERT and XLM-R as network backbones. Our results show that COSY achieves the state-of-the-art performance for both tasks, without using auxiliary training data.

EnsLM: Ensemble Language Model for Data Diversity by Semantic Clustering
Zhibin Duan | Hao Zhang | Chaojie Wang | Zhengjue Wang | Bo Chen | Mingyuan Zhou
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)

Natural language processing (NLP) often faces the problem of data diversity such as different domains, themes, styles, and so on. Therefore, a single language model (LM) is insufficient to learn all knowledge from diverse samples. To solve this problem, we firstly propose an autoencoding topic model with a mixture prior (mATM) to perform clustering for the data, where the clusters defined in semantic space describes the data diversity. Having obtained the clustering assignment for each sample, we develop the ensemble LM (EnsLM) with the technique of weight modulation. Specifically, EnsLM contains a backbone that is adjusted by a few modulated weights to fit for different sample clusters. As a result, the backbone learns the shared knowledge among all clusters while modulated weights extract the cluster-specific features. EnsLM can be trained jointly with mATM with a flexible LM backbone. We evaluate the effectiveness of both mATM and EnsLM on various tasks.

PhotoChat: A Human-Human Dialogue Dataset With Photo Sharing Behavior For Joint Image-Text Modeling
Xiaoxue Zang | Lijuan Liu | Maria Wang | Yang Song | Hao Zhang | Jindong Chen
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)

We present a new human-human dialogue dataset - PhotoChat, the first dataset that casts light on the photo sharing behavior in online messaging. PhotoChat contains 12k dialogues, each of which is paired with a user photo that is shared during the conversation. Based on this dataset, we propose two tasks to facilitate research on image-text modeling: a photo-sharing intent prediction task that predicts whether one intends to share a photo in the next conversation turn, and a photo retrieval task that retrieves the most relevant photo according to the dialogue context. In addition, for both tasks, we provide baseline models using the state-of-the-art models and report their benchmark performances. The best image retrieval model achieves 10.4% recall@1 (out of 1000 candidates) and the best photo intent prediction model achieves 58.1% F1 score, indicating that the dataset presents interesting yet challenging real-world problems. We are releasing PhotoChat to facilitate future research work among the community.


Span-based Localizing Network for Natural Language Video Localization
Hao Zhang | Aixin Sun | Wei Jing | Joey Tianyi Zhou
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

Given an untrimmed video and a text query, natural language video localization (NLVL) is to locate a matching span from the video that semantically corresponds to the query. Existing solutions formulate NLVL either as a ranking task and apply multimodal matching architecture, or as a regression task to directly regress the target video span. In this work, we address NLVL task with a span-based QA approach by treating the input video as text passage. We propose a video span localizing network (VSLNet), on top of the standard span-based QA framework, to address NLVL. The proposed VSLNet tackles the differences between NLVL and span-based QA through a simple and yet effective query-guided highlighting (QGH) strategy. The QGH guides VSLNet to search for matching video span within a highlighted region. Through extensive experiments on three benchmark datasets, we show that the proposed VSLNet outperforms the state-of-the-art methods; and adopting span-based QA framework is a promising direction to solve NLVL.

Multi-source Meta Transfer for Low Resource Multiple-Choice Question Answering
Ming Yan | Hao Zhang | Di Jin | Joey Tianyi Zhou
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

Multiple-choice question answering (MCQA) is one of the most challenging tasks in machine reading comprehension since it requires more advanced reading comprehension skills such as logical reasoning, summarization, and arithmetic operations. Unfortunately, most existing MCQA datasets are small in size, which increases the difficulty of model learning and generalization. To address this challenge, we propose a multi-source meta transfer (MMT) for low-resource MCQA. In this framework, we first extend meta learning by incorporating multiple training sources to learn a generalized feature representation across domains. To bridge the distribution gap between training sources and the target, we further introduce the meta transfer that can be integrated into the multi-source meta training. More importantly, the proposed MMT is independent of backbone language models. Extensive experiments demonstrate the superiority of MMT over state-of-the-arts, and continuous improvements can be achieved on different backbone networks on both supervised and unsupervised domain adaptation settings.

Semi-supervised URL Segmentation with Recurrent Neural Networks Pre-trained on Knowledge Graph Entities
Hao Zhang | Jae Ro | Richard Sproat
Proceedings of the 28th International Conference on Computational Linguistics

Breaking domain names such as openresearch into component words open and research is important for applications like Text-to-Speech synthesis and web search. We link this problem to the classic problem of Chinese word segmentation and show the effectiveness of a tagging model based on Recurrent Neural Networks (RNNs) using characters as input. To compensate for the lack of training data, we propose a pre-training method on concatenated entity names in a large knowledge database. Pre-training improves the model by 33% and brings the sequence accuracy to 85%.

Friendly Topic Assistant for Transformer Based Abstractive Summarization
Zhengjue Wang | Zhibin Duan | Hao Zhang | Chaojie Wang | Long Tian | Bo Chen | Mingyuan Zhou
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Abstractive document summarization is a comprehensive task including document understanding and summary generation, in which area Transformer-based models have achieved the state-of-the-art performance. Compared with Transformers, topic models are better at learning explicit document semantics, and hence could be integrated into Transformers to further boost their performance. To this end, we rearrange and explore the semantics learned by a topic model, and then propose a topic assistant (TA) including three modules. TA is compatible with various Transformer-based models and user-friendly since i) TA is a plug-and-play model that does not break any structure of the original Transformer network, making users easily fine-tune Transformer+TA based on a well pre-trained model; ii) TA only introduces a small number of extra parameters. Experimental results on three datasets demonstrate that TA is able to improve the performance of several Transformer-based models.


Dual Adversarial Neural Transfer for Low-Resource Named Entity Recognition
Joey Tianyi Zhou | Hao Zhang | Di Jin | Hongyuan Zhu | Meng Fang | Rick Siow Mong Goh | Kenneth Kwok
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

We propose a new neural transfer method termed Dual Adversarial Transfer Network (DATNet) for addressing low-resource Named Entity Recognition (NER). Specifically, two variants of DATNet, i.e., DATNet-F and DATNet-P, are investigated to explore effective feature fusion between high and low resource. To address the noisy and imbalanced training data, we propose a novel Generalized Resource-Adversarial Discriminator (GRAD). Additionally, adversarial training is adopted to boost model generalization. In experiments, we examine the effects of different components in DATNet across domains and languages and show that significant improvement can be obtained especially for low-resource data, without augmenting any additional hand-crafted features and pre-trained language model.

Neural Models of Text Normalization for Speech Applications
Hao Zhang | Richard Sproat | Axel H. Ng | Felix Stahlberg | Xiaochang Peng | Kyle Gorman | Brian Roark
Computational Linguistics, Volume 45, Issue 2 - June 2019

Machine learning, including neural network techniques, have been applied to virtually every domain in natural language processing. One problem that has been somewhat resistant to effective machine learning solutions is text normalization for speech applications such as text-to-speech synthesis (TTS). In this application, one must decide, for example, that 123 is verbalized as one hundred twenty three in 123 pages but as one twenty three in 123 King Ave. For this task, state-of-the-art industrial systems depend heavily on hand-written language-specific grammars.We propose neural network models that treat text normalization for TTS as a sequence-to-sequence problem, in which the input is a text token in context, and the output is the verbalization of that token. We find that the most effective model, in accuracy and efficiency, is one where the sentential context is computed once and the results of that computation are combined with the computation of each token in sequence to compute the verbalization. This model allows for a great deal of flexibility in terms of representing the context, and also allows us to integrate tagging and segmentation into the process.These models perform very well overall, but occasionally they will predict wildly inappropriate verbalizations, such as reading 3 cm as three kilometers. Although rare, such verbalizations are a major issue for TTS applications. We thus use finite-state covering grammars to guide the neural models, either during training and decoding, or just during decoding, away from such “unrecoverable” errors. Such grammars can largely be learned from data.


WordNet Troponymy and Extraction of “Manner-Result” Relations
Aliaksandr Huminski | Hao Zhang
Proceedings of the 9th Global Wordnet Conference

Commonsense knowledge bases need to have relations that allow to predict the consequences of specific actions (say, if John stabbed Peter, Peter might be killed) and to unfold the possible actions for the specific results (Peter was killed. It could happen because of poisoning, stabbing, shooting, etc.) This kind of causal relations are established between manner verbs and result verbs: manner-result relations. We offer a procedure on how to extract manner-result relations from WordNet through the analysis of the troponym glosses. The procedure of extraction includes three steps and the results are based on the analysis of the whole set of verbs in WordNet.

Fast and Accurate Reordering with ITG Transition RNN
Hao Zhang | Axel Ng | Richard Sproat
Proceedings of the 27th International Conference on Computational Linguistics

Attention-based sequence-to-sequence neural network models learn to jointly align and translate. The quadratic-time attention mechanism is powerful as it is capable of handling arbitrary long-distance reordering, but computationally expensive. In this paper, towards making neural translation both accurate and efficient, we follow the traditional pre-reordering approach to decouple reordering from translation. We add a reordering RNN that shares the input encoder with the decoder. The RNNs are trained jointly with a multi-task loss function and applied sequentially at inference time. The task of the reordering model is to predict the permutation of the input words following the target language word order. After reordering, the attention in the decoder becomes more peaked and monotonic. For reordering, we adopt the Inversion Transduction Grammars (ITG) and propose a transition system to parse input to trees for reordering. We harness the ITG transition system with RNN. With the modeling power of RNN, we achieve superior reordering accuracy without any feature engineering. In experiments, we apply the model to the task of text normalization. Compared to a strong baseline of attention-based RNN, our ITG RNN re-ordering model can reach the same reordering accuracy with only 1/10 of the training data and is 2.5x faster in decoding.

UKP-Athene: Multi-Sentence Textual Entailment for Claim Verification
Andreas Hanselowski | Hao Zhang | Zile Li | Daniil Sorokin | Benjamin Schiller | Claudia Schulz | Iryna Gurevych
Proceedings of the First Workshop on Fact Extraction and VERification (FEVER)

The Fact Extraction and VERification (FEVER) shared task was launched to support the development of systems able to verify claims by extracting supporting or refuting facts from raw text. The shared task organizers provide a large-scale dataset for the consecutive steps involved in claim verification, in particular, document retrieval, fact extraction, and claim classification. In this paper, we present our claim verification pipeline approach, which, according to the preliminary results, scored third in the shared task, out of 23 competing systems. For the document retrieval, we implemented a new entity linking approach. In order to be able to rank candidate facts and classify a claim on the basis of several selected facts, we introduce two extensions to the Enhanced LSTM (ESIM).


Learning Concept Taxonomies from Multi-modal Data
Hao Zhang | Zhiting Hu | Yuntian Deng | Mrinmaya Sachan | Zhicheng Yan | Eric Xing
Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)


KWB: An Automated Quick News System for Chinese Readers
Yiqi Bai | Wenjing Yang | Hao Zhang | Jingwen Wang | Ming Jia | Roland Tong | Jie Wang
Proceedings of the Eighth SIGHAN Workshop on Chinese Language Processing


Enforcing Structural Diversity in Cube-pruned Dependency Parsing
Hao Zhang | Ryan McDonald
Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)


Online Learning for Inexact Hypergraph Search
Hao Zhang | Liang Huang | Kai Zhao | Ryan McDonald
Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing

Universal Dependency Annotation for Multilingual Parsing
Ryan McDonald | Joakim Nivre | Yvonne Quirmbach-Brundage | Yoav Goldberg | Dipanjan Das | Kuzman Ganchev | Keith Hall | Slav Petrov | Hao Zhang | Oscar Täckström | Claudia Bedini | Núria Bertomeu Castelló | Jungmee Lee
Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)


NiuTrans: An Open Source Toolkit for Phrase-based and Syntax-based Machine Translation
Tong Xiao | Jingbo Zhu | Hao Zhang | Qiang Li
Proceedings of the ACL 2012 System Demonstrations

Generalized Higher-Order Dependency Parsing with Cube Pruning
Hao Zhang | Ryan McDonald
Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning


Document-level Consistency Verification in Machine Translation
Tong Xiao | Jingbo Zhu | Shujie Yao | Hao Zhang
Proceedings of Machine Translation Summit XIII: Papers

Binarized Forest to String Translation
Hao Zhang | Licheng Fang | Peng Xu | Xiaoyun Wu
Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies


NEUNLPLab Chinese Word Sense Induction System for SIGHAN Bakeoff 2010
Hao Zhang | Tong Xiao | Jingbo Zhu
CIPS-SIGHAN Joint Conference on Chinese Language Processing

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


Binarization of Synchronous Context-Free Grammars
Liang Huang | Hao Zhang | Daniel Gildea | Kevin Knight
Computational Linguistics, Volume 35, Number 4, December 2009


Bayesian Learning of Non-Compositional Phrases with Synchronous Parsing
Hao Zhang | Chris Quirk | Robert C. Moore | Daniel Gildea
Proceedings of ACL-08: HLT

Efficient Multi-Pass Decoding for Synchronous Context Free Grammars
Hao Zhang | Daniel Gildea
Proceedings of ACL-08: HLT

Extracting Synchronous Grammar Rules From Word-Level Alignments in Linear Time
Hao Zhang | Daniel Gildea | David Chiang
Proceedings of the 22nd International Conference on Computational Linguistics (Coling 2008)


Factorization of Synchronous Context-Free Grammars in Linear Time
Hao Zhang | Daniel Gildea
Proceedings of SSST, NAACL-HLT 2007 / AMTA Workshop on Syntax and Structure in Statistical Translation


Factoring Synchronous Grammars by Sorting
Daniel Gildea | Giorgio Satta | Hao Zhang
Proceedings of the COLING/ACL 2006 Main Conference Poster Sessions

Inducing Word Alignments with Bilexical Synchronous Trees
Hao Zhang | Daniel Gildea
Proceedings of the COLING/ACL 2006 Main Conference Poster Sessions

Efficient Search for Inversion Transduction Grammar
Hao Zhang | Daniel Gildea
Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing

Synchronous Binarization for Machine Translation
Hao Zhang | Liang Huang | Daniel Gildea | Kevin Knight
Proceedings of the Human Language Technology Conference of the NAACL, Main Conference


Stochastic Lexicalized Inversion Transduction Grammar for Alignment
Hao Zhang | Daniel Gildea
Proceedings of the 43rd Annual Meeting of the Association for Computational Linguistics (ACL’05)

Machine Translation as Lexicalized Parsing with Hooks
Liang Huang | Hao Zhang | Daniel Gildea
Proceedings of the Ninth International Workshop on Parsing Technology


Syntax-Based Alignment: Supervised or Unsupervised?
Hao Zhang | Daniel Gildea
COLING 2004: Proceedings of the 20th International Conference on Computational Linguistics


Chinese Lexical Analysis Using Hierarchical Hidden Markov Model
Hua-Ping Zhang | Qun Liu | Xue-Qi Cheng | Hao Zhang | Hong-Kui Yu
Proceedings of the Second SIGHAN Workshop on Chinese Language Processing


Automatic Recognition of Chinese Unknown Words Based on Roles Tagging
Kevin Zhang | Qun Liu | Hao Zhang | Xue-Qi Cheng
COLING-02: The First SIGHAN Workshop on Chinese Language Processing