Search Results for author: Boxing Chen

Found 38 papers, 11 papers with code

Bridging the Gap: Cross-Lingual Summarization with Compression Rate

no code implementations15 Oct 2021 Yu Bai, Heyan Huang, Kai Fan, Yang Gao, Zewen Chi, Boxing Chen

In this paper, we propose a novel task, Cross-lingual Summarization with Compression rate (CSC), to benefit cross-lingual summarization through large-scale MT corpus.

Data Augmentation Machine Translation +1

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

Context-Interactive Pre-Training for Document Machine Translation

no code implementations NAACL 2021 Pengcheng Yang, Pei Zhang, Boxing Chen, Jun Xie, Weihua Luo

Document machine translation aims to translate the source sentence into the target language in the presence of additional contextual information.

Machine Translation Translation

Continual Learning for Neural Machine Translation

no code implementations NAACL 2021 Yue Cao, Hao-Ran Wei, Boxing Chen, Xiaojun Wan

In practical applications, NMT models are usually trained on a general domain corpus and then fine-tuned by continuing training on the in-domain corpus.

Continual Learning Knowledge Distillation +2

G-Transformer for Document-level Machine Translation

1 code implementation ACL 2021 Guangsheng Bao, Yue Zhang, Zhiyang Teng, Boxing Chen, Weihua Luo

However, study shows that when we further enlarge the translation unit to a whole document, supervised training of Transformer can fail.

Document-level Document Level Machine Translation +2

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

Exploiting Neural Query Translation into Cross Lingual Information Retrieval

no code implementations26 Oct 2020 Liang Yao, Baosong Yang, Haibo Zhang, Weihua Luo, Boxing Chen

As a crucial role in cross-language information retrieval (CLIR), query translation has three main challenges: 1) the adequacy of translation; 2) the lack of in-domain parallel training data; and 3) the requisite of low latency.

Data Augmentation Domain Adaptation +3

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

Self-Paced Learning for Neural Machine Translation

1 code implementation EMNLP 2020 Yu Wan, Baosong Yang, Derek F. Wong, Yikai Zhou, Lidia S. Chao, Haibo Zhang, Boxing Chen

Recent studies have proven that the training of neural machine translation (NMT) can be facilitated by mimicking the learning process of humans.

Curriculum Learning Machine Translation +1

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

Long-Short Term Masking Transformer: A Simple but Effective Baseline for Document-level Neural Machine Translation

no code implementations EMNLP 2020 Pei Zhang, Boxing Chen, Niyu Ge, Kai Fan

In this paper, we research extensively the pros and cons of the standard transformer in document-level translation, and find that the auto-regressive property can simultaneously bring both the advantage of the consistency and the disadvantage of error accumulation.

Document-level Machine Translation +1

Bilingual Dictionary Based Neural Machine Translation without Using Parallel Sentences

1 code implementation ACL 2020 Xiangyu Duan, Baijun Ji, Hao Jia, Min Tan, Min Zhang, Boxing Chen, Weihua Luo, Yue Zhang

In this paper, we propose a new task of machine translation (MT), which is based on no parallel sentences but can refer to a ground-truth bilingual dictionary.

Machine Translation Translation

Visual Agreement Regularized Training for Multi-Modal Machine Translation

no code implementations27 Dec 2019 Pengcheng Yang, Boxing Chen, Pei Zhang, Xu sun

Further analysis demonstrates that the proposed regularized training can effectively improve the agreement of attention on the image, leading to better use of visual information.

Machine Translation 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

Neural Zero-Inflated Quality Estimation Model For Automatic Speech Recognition System

no code implementations3 Oct 2019 Kai Fan, Jiayi Wang, Bo Li, Shiliang Zhang, Boxing Chen, Niyu Ge, Zhijie Yan

The performances of automatic speech recognition (ASR) systems are usually evaluated by the metric word error rate (WER) when the manually transcribed data are provided, which are, however, expensively available in the real scenario.

automatic-speech-recognition Language Modelling +3

Zero-Shot Cross-Lingual Abstractive Sentence Summarization through Teaching Generation and Attention

1 code implementation ACL 2019 Xiangyu Duan, Mingming Yin, Min Zhang, Boxing Chen, Weihua Luo

But there is no cross-lingual parallel corpus, whose source sentence language is different to the summary language, to directly train a cross-lingual ASSUM system.

Sentence Summarization Translation

Lattice Transformer for Speech Translation

no code implementations ACL 2019 Pei Zhang, Boxing Chen, Niyu Ge, Kai Fan

Recent advances in sequence modeling have highlighted the strengths of the transformer architecture, especially in achieving state-of-the-art machine translation results.

automatic-speech-recognition Machine Translation +2

Alibaba Submission to the WMT18 Parallel Corpus Filtering Task

no code implementations WS 2018 Jun Lu, Xiaoyu Lv, Yangbin Shi, Boxing Chen

This paper describes the Alibaba Machine Translation Group submissions to the WMT 2018 Shared Task on Parallel Corpus Filtering.

Machine Translation Translation +1

Alibaba Submission for WMT18 Quality Estimation Task

no code implementations WS 2018 Jiayi Wang, Kai Fan, Bo Li, Fengming Zhou, Boxing Chen, Yangbin Shi, Luo Si

The goal of WMT 2018 Shared Task on Translation Quality Estimation is to investigate automatic methods for estimating the quality of machine translation results without reference translations.

Automatic Post-Editing Language Modelling +1

"Bilingual Expert" Can Find Translation Errors

1 code implementation25 Jul 2018 Kai Fan, Jiayi Wang, Bo Li, Fengming Zhou, Boxing Chen, Luo Si

Recent advances in statistical machine translation via the adoption of neural sequence-to-sequence models empower the end-to-end system to achieve state-of-the-art in many WMT benchmarks.

Language Modelling Machine Translation +1

Cost Weighting for Neural Machine Translation Domain Adaptation

no code implementations WS 2017 Boxing Chen, Colin Cherry, George Foster, Samuel Larkin

We compare cost weighting to two traditional domain adaptation techniques developed for statistical machine translation: data selection and sub-corpus weighting.

Domain Adaptation Machine Translation +1

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