Search Results for author: Liang Ding

Found 24 papers, 7 papers with code

On the Complementarity between Pre-Training and Back-Translation for Neural Machine Translation

1 code implementation5 Oct 2021 Xuebo Liu, Longyue Wang, Derek F. Wong, Liang Ding, Lidia S. Chao, Shuming Shi, Zhaopeng Tu

Pre-training (PT) and back-translation (BT) are two simple and powerful methods to utilize monolingual data for improving the model performance of neural machine translation (NMT).

Machine Translation Translation

Improving Neural Machine Translation by Bidirectional Training

no code implementations16 Sep 2021 Liang Ding, Di wu, DaCheng Tao

We present a simple and effective pretraining strategy -- bidirectional training (BiT) for neural machine translation.

Machine Translation Translation

On the Copying Behaviors of Pre-Training for Neural Machine Translation

1 code implementation17 Jul 2021 Xuebo Liu, Longyue Wang, Derek F. Wong, Liang Ding, Lidia S. Chao, Shuming Shi, Zhaopeng Tu

In response to this problem, we propose a simple and effective method named copying penalty to control the copying behaviors in decoding.

Machine Translation Translation

Progressive Multi-Granularity Training for Non-Autoregressive Translation

no code implementations10 Jun 2021 Liang Ding, Longyue Wang, Xuebo Liu, Derek F. Wong, DaCheng Tao, Zhaopeng Tu

Non-autoregressive translation (NAT) significantly accelerates the inference process via predicting the entire target sequence.

Translation

Rejuvenating Low-Frequency Words: Making the Most of Parallel Data in Non-Autoregressive Translation

no code implementations ACL 2021 Liang Ding, Longyue Wang, Xuebo Liu, Derek F. Wong, DaCheng Tao, Zhaopeng Tu

Results demonstrate that the proposed approach can significantly and universally improve translation quality by reducing translation errors on low-frequency words.

Knowledge Distillation Translation

Self-Guided Curriculum Learning for Neural Machine Translation

no code implementations10 May 2021 Lei Zhou, Liang Ding, Kevin Duh, Shinji Watanabe, Ryohei Sasano, Koichi Takeda

In the field of machine learning, the well-trained model is assumed to be able to recover the training labels, i. e. the synthetic labels predicted by the model should be as close to the ground-truth labels as possible.

Curriculum Learning Machine Translation +1

Bridging the Gap Between Clean Data Training and Real-World Inference for Spoken Language Understanding

no code implementations13 Apr 2021 Di wu, Yiren Chen, Liang Ding, DaCheng Tao

Spoken language understanding (SLU) system usually consists of various pipeline components, where each component heavily relies on the results of its upstream ones.

automatic-speech-recognition Denoising +5

Towards Efficiently Diversifying Dialogue Generation via Embedding Augmentation

1 code implementation2 Mar 2021 Yu Cao, Liang Ding, Zhiliang Tian, Meng Fang

Dialogue generation models face the challenge of producing generic and repetitive responses.

Dialogue Generation

Unsupervised Word Alignment via Cross-Lingual Contrastive Learning

no code implementations1 Jan 2021 Di wu, Liang Ding, Shuo Yang, DaCheng Tao

Recently, the performance of the neural word alignment models has exceeded that of statistical models.

Contrastive Learning Translation +1

Understanding and Improving Lexical Choice in Non-Autoregressive Translation

no code implementations ICLR 2021 Liang Ding, Longyue Wang, Xuebo Liu, Derek F. Wong, DaCheng Tao, Zhaopeng Tu

To this end, we introduce an extra Kullback-Leibler divergence term derived by comparing the lexical choice of NAT model and that embedded in the raw data.

Knowledge Distillation Translation

Understanding and Improving Encoder Layer Fusion in Sequence-to-Sequence Learning

1 code implementation ICLR 2021 Xuebo Liu, Longyue Wang, Derek F. Wong, Liang Ding, Lidia S. Chao, Zhaopeng Tu

Encoder layer fusion (EncoderFusion) is a technique to fuse all the encoder layers (instead of the uppermost layer) for sequence-to-sequence (Seq2Seq) models, which has proven effective on various NLP tasks.

Grammatical Error Correction Machine Translation +3

Context-Aware Cross-Attention for Non-Autoregressive Translation

1 code implementation COLING 2020 Liang Ding, Longyue Wang, Di wu, DaCheng Tao, Zhaopeng Tu

Non-autoregressive translation (NAT) significantly accelerates the inference process by predicting the entire target sequence.

Translation

Sample and Computationally Efficient Simulation Metamodeling in High Dimensions

no code implementations14 Oct 2020 Liang Ding, Xiaowei Zhang

Stochastic kriging has been widely employed for simulation metamodeling to predict the response surface of a complex simulation model.

Zero-Shot Translation Quality Estimation with Explicit Cross-Lingual Patterns

no code implementations10 Oct 2020 Lei Zhou, Liang Ding, Koichi Takeda

In response to this issue, we propose to expose explicit cross-lingual patterns, \textit{e. g.} word alignments and generation score, to our proposed zero-shot models.

Translation

Overcoming the Curse of Dimensionality in Density Estimation with Mixed Sobolev GANs

no code implementations5 Jun 2020 Liang Ding, Rui Tuo, Shahin Shahrampour

We propose a novel GAN framework for non-parametric density estimation with high-dimensional data.

Density Estimation

Self-Attention with Cross-Lingual Position Representation

no code implementations ACL 2020 Liang Ding, Long-Yue Wang, DaCheng Tao

Position encoding (PE), an essential part of self-attention networks (SANs), is used to preserve the word order information for natural language processing tasks, generating fixed position indices for input sequences.

Machine Translation Translation

Recurrent Graph Syntax Encoder for Neural Machine Translation

no code implementations19 Aug 2019 Liang Ding, DaCheng Tao

Syntax-incorporated machine translation models have been proven successful in improving the model's reasoning and meaning preservation ability.

Machine Translation Translation

Efficient Learning of Optimal Markov Network Topology with k-Tree Modeling

1 code implementation21 Jan 2018 Liang Ding, Di Chang, Russell Malmberg, Aaron Martinez, David Robinson, Matthew Wicker, Hongfei Yan, Liming Cai

The seminal work of Chow and Liu (1968) shows that approximation of a finite probabilistic system by Markov trees can achieve the minimum information loss with the topology of a maximum spanning tree.

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