Search Results for author: Zhirui Zhang

Found 21 papers, 7 papers with code

Non-Parametric Online Learning from Human Feedback for Neural Machine Translation

no code implementations23 Sep 2021 Dongqi Wang, Haoran Wei, Zhirui Zhang, ShuJian Huang, Jun Xie, Weihua Luo, Jiajun Chen

We study the problem of online learning with human feedback in the human-in-the-loop machine translation, in which the human translators revise the machine-generated translations and then the corrected translations are used to improve the neural machine translation (NMT) system.

Machine Translation Translation

Non-Parametric Unsupervised Domain Adaptation for Neural Machine Translation

1 code implementation14 Sep 2021 Xin Zheng, Zhirui Zhang, ShuJian Huang, Boxing Chen, Jun Xie, Weihua Luo, Jiajun Chen

Recently, $k$NN-MT has shown the promising capability of directly incorporating the pre-trained neural machine translation (NMT) model with domain-specific token-level $k$-nearest-neighbor ($k$NN) retrieval to achieve domain adaptation without retraining.

Machine Translation Translation +1

Rethinking Zero-shot Neural Machine Translation: From a Perspective of Latent Variables

1 code implementation10 Sep 2021 Weizhi Wang, Zhirui Zhang, Yichao Du, Boxing Chen, Jun Xie, Weihua Luo

However, it usually suffers from capturing spurious correlations between the output language and language invariant semantics due to the maximum likelihood training objective, leading to poor transfer performance on zero-shot translation.

Denoising Machine Translation +1

Task-Oriented Dialogue System as Natural Language Generation

1 code implementation31 Aug 2021 Weizhi Wang, Zhirui Zhang, Junliang Guo, Yinpei Dai, Boxing Chen, Weihua Luo

In this paper, we propose to formulate the task-oriented dialogue system as the purely natural language generation task, so as to fully leverage the large-scale pre-trained models like GPT-2 and simplify complicated delexicalization prepossessing.

Text Generation Transfer Learning

Adaptive Nearest Neighbor Machine Translation

1 code implementation ACL 2021 Xin Zheng, Zhirui Zhang, Junliang Guo, ShuJian Huang, Boxing Chen, Weihua Luo, Jiajun Chen

On four benchmark machine translation datasets, we demonstrate that the proposed method is able to effectively filter out the noises in retrieval results and significantly outperforms the vanilla kNN-MT model.

Machine Translation Translation

Towards Variable-Length Textual Adversarial Attacks

no code implementations16 Apr 2021 Junliang Guo, Zhirui Zhang, Linlin Zhang, Linli Xu, Boxing Chen, Enhong Chen, Weihua Luo

In this way, our approach is able to more comprehensively find adversarial examples around the decision boundary and effectively conduct adversarial attacks.

Machine Translation Translation

Incorporating BERT into Parallel Sequence Decoding with Adapters

1 code implementation NeurIPS 2020 Junliang Guo, Zhirui Zhang, Linli Xu, Hao-Ran Wei, Boxing Chen, Enhong Chen

Our framework is based on a parallel sequence decoding algorithm named Mask-Predict considering the bi-directional and conditional independent nature of BERT, and can be adapted to traditional autoregressive decoding easily.

Machine Translation Natural Language Understanding +2

Iterative Domain-Repaired Back-Translation

no code implementations EMNLP 2020 Hao-Ran Wei, Zhirui Zhang, Boxing Chen, Weihua Luo

In this paper, we focus on the domain-specific translation with low resources, where in-domain parallel corpora are scarce or nonexistent.

Domain Adaptation Translation

Cross-lingual Pre-training Based Transfer for Zero-shot Neural Machine Translation

no code implementations3 Dec 2019 Baijun Ji, Zhirui Zhang, Xiangyu Duan, Min Zhang, Boxing Chen, Weihua Luo

However, existing transfer methods involving a common target language are far from success in the extreme scenario of zero-shot translation, due to the language space mismatch problem between transferor (the parent model) and transferee (the child model) on the source side.

Machine Translation Transfer Learning +1

Budgeted Policy Learning for Task-Oriented Dialogue Systems

no code implementations ACL 2019 Zhirui Zhang, Xiujun Li, Jianfeng Gao, Enhong Chen

This paper presents a new approach that extends Deep Dyna-Q (DDQ) by incorporating a Budget-Conscious Scheduling (BCS) to best utilize a fixed, small amount of user interactions (budget) for learning task-oriented dialogue agents.

Task-Oriented Dialogue Systems

Unsupervised Neural Machine Translation with SMT as Posterior Regularization

1 code implementation14 Jan 2019 Shuo Ren, Zhirui Zhang, Shujie Liu, Ming Zhou, Shuai Ma

To address this issue, we introduce phrase based Statistic Machine Translation (SMT) models which are robust to noisy data, as posterior regularizations to guide the training of unsupervised NMT models in the iterative back-translation process.

Translation Unsupervised Machine Translation

Bidirectional Generative Adversarial Networks for Neural Machine Translation

no code implementations CONLL 2018 Zhirui Zhang, Shujie Liu, Mu Li, Ming Zhou, Enhong Chen

To address this issue and stabilize the GAN training, in this paper, we propose a novel Bidirectional Generative Adversarial Network for Neural Machine Translation (BGAN-NMT), which aims to introduce a generator model to act as the discriminator, whereby the discriminator naturally considers the entire translation space so that the inadequate training problem can be alleviated.

Language Modelling Machine Translation +1

Approximate Distribution Matching for Sequence-to-Sequence Learning

no code implementations24 Aug 2018 Wenhu Chen, Guanlin Li, Shujie Liu, Zhirui Zhang, Mu Li, Ming Zhou

Then, we interpret sequence-to-sequence learning as learning a transductive model to transform the source local latent distributions to match their corresponding target distributions.

Image Captioning Machine Translation +1

Style Transfer as Unsupervised Machine Translation

no code implementations23 Aug 2018 Zhirui Zhang, Shuo Ren, Shujie Liu, Jianyong Wang, Peng Chen, Mu Li, Ming Zhou, Enhong Chen

Language style transferring rephrases text with specific stylistic attributes while preserving the original attribute-independent content.

Style Transfer Translation +1

Regularizing Neural Machine Translation by Target-bidirectional Agreement

no code implementations13 Aug 2018 Zhirui Zhang, Shuangzhi Wu, Shujie Liu, Mu Li, Ming Zhou, Tong Xu

Although Neural Machine Translation (NMT) has achieved remarkable progress in the past several years, most NMT systems still suffer from a fundamental shortcoming as in other sequence generation tasks: errors made early in generation process are fed as inputs to the model and can be quickly amplified, harming subsequent sequence generation.

Machine Translation Translation

Generative Bridging Network for Neural Sequence Prediction

no code implementations NAACL 2018 Wenhu Chen, Guanlin Li, Shuo Ren, Shujie Liu, Zhirui Zhang, Mu Li, Ming Zhou

In order to alleviate data sparsity and overfitting problems in maximum likelihood estimation (MLE) for sequence prediction tasks, we propose the Generative Bridging Network (GBN), in which a novel bridge module is introduced to assist the training of the sequence prediction model (the generator network).

Abstractive Text Summarization Image Captioning +5

Learning to Collaborate for Question Answering and Asking

no code implementations NAACL 2018 Duyu Tang, Nan Duan, Zhao Yan, Zhirui Zhang, Yibo Sun, Shujie Liu, Yuanhua Lv, Ming Zhou

Secondly, directly applying GAN that regards all the generated questions as negative instances could not improve the accuracy of the QA model.

Answer Selection Question Generation

Joint Training for Neural Machine Translation Models with Monolingual Data

no code implementations1 Mar 2018 Zhirui Zhang, Shujie Liu, Mu Li, Ming Zhou, Enhong Chen

Monolingual data have been demonstrated to be helpful in improving translation quality of both statistical machine translation (SMT) systems and neural machine translation (NMT) systems, especially in resource-poor or domain adaptation tasks where parallel data are not rich enough.

Domain Adaptation Machine Translation +1

Stack-based Multi-layer Attention for Transition-based Dependency Parsing

no code implementations EMNLP 2017 Zhirui Zhang, Shujie Liu, Mu Li, Ming Zhou, Enhong Chen

Although sequence-to-sequence (seq2seq) network has achieved significant success in many NLP tasks such as machine translation and text summarization, simply applying this approach to transition-based dependency parsing cannot yield a comparable performance gain as in other state-of-the-art methods, such as stack-LSTM and head selection.

Language Modelling Machine Translation +3

Generative Bridging Network in Neural Sequence Prediction

no code implementations28 Jun 2017 Wenhu Chen, Guanlin Li, Shuo Ren, Shujie Liu, Zhirui Zhang, Mu Li, Ming Zhou

In order to alleviate data sparsity and overfitting problems in maximum likelihood estimation (MLE) for sequence prediction tasks, we propose the Generative Bridging Network (GBN), in which a novel bridge module is introduced to assist the training of the sequence prediction model (the generator network).

Abstractive Text Summarization Language Modelling +2

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