Search Results for author: Lijun Wu

Found 44 papers, 21 papers with code

Machine Translation With Weakly Paired Bilingual Documents

no code implementations ICLR 2019 Lijun Wu, Jinhua Zhu, Di He, Fei Gao, Xu Tan, Tao Qin, Tie-Yan Liu

Neural machine translation, which achieves near human-level performance in some languages, strongly relies on the availability of large amounts of parallel sentences, which hinders its applicability to low-resource language pairs.

Translation Unsupervised Machine Translation

mixSeq: A Simple Data Augmentation Methodfor Neural Machine Translation

no code implementations ACL (IWSLT) 2021 Xueqing Wu, Yingce Xia, Jinhua Zhu, Lijun Wu, Shufang Xie, Yang Fan, Tao Qin

Data augmentation, which refers to manipulating the inputs (e. g., adding random noise, masking specific parts) to enlarge the dataset, has been widely adopted in machine learning.

Data Augmentation Machine Translation +1

How to leverage the multimodal EHR data for better medical prediction?

no code implementations EMNLP 2021 Bo Yang, Lijun Wu

Therefore, in this paper, we first extract the accompanying clinical notes from EHR and propose a method to integrate these data, we also comprehensively study the different models and the data leverage methods for better medical task prediction performance.

RetroGraph: Retrosynthetic Planning with Graph Search

1 code implementation23 Jun 2022 Shufang Xie, Rui Yan, Peng Han, Yingce Xia, Lijun Wu, Chenjuan Guo, Bin Yang, Tao Qin

We observe that the same intermediate molecules are visited many times in the searching process, and they are usually independently treated in previous tree-based methods (e. g., AND-OR tree search, Monte Carlo tree search).

Drug Discovery

A Survey on Non-Autoregressive Generation for Neural Machine Translation and Beyond

1 code implementation20 Apr 2022 Yisheng Xiao, Lijun Wu, Junliang Guo, Juntao Li, Min Zhang, Tao Qin, Tie-Yan Liu

While NAR generation can significantly accelerate inference speed for machine translation, the speedup comes at the cost of sacrificed translation accuracy compared to its counterpart, auto-regressive (AR) generation.

Automatic Speech Recognition Dialogue Generation +6

A Mutually Reinforced Framework for Pretrained Sentence Embeddings

no code implementations28 Feb 2022 Junhan Yang, Zheng Liu, Shitao Xiao, Jianxun Lian, Lijun Wu, Defu Lian, Guangzhong Sun, Xing Xie

Instead of relying on annotation heuristics defined by humans, it leverages the sentence representation model itself and realizes the following iterative self-supervision process: on one hand, the improvement of sentence representation may contribute to the quality of data annotation; on the other hand, more effective data annotation helps to generate high-quality positive samples, which will further improve the current sentence representation model.

Contrastive Learning Sentence Embeddings

AF$_2$: Adaptive Focus Framework for Aerial Imagery Segmentation

no code implementations18 Feb 2022 Lin Huang, Qiyuan Dong, Lijun Wu, Jia Zhang, Jiang Bian, Tie-Yan Liu

As a specific semantic segmentation task, aerial imagery segmentation has been widely employed in high spatial resolution (HSR) remote sensing images understanding.

Semantic Segmentation

Dynamic Relation Discovery and Utilization in Multi-Entity Time Series Forecasting

no code implementations18 Feb 2022 Lin Huang, Lijun Wu, Jia Zhang, Jiang Bian, Tie-Yan Liu

How to discover the useful implicit relation between entities and effectively utilize the relations for each entity under various circumstances is crucial.

Graph Learning Time Series +1

Direct Molecular Conformation Generation

1 code implementation3 Feb 2022 Jinhua Zhu, Yingce Xia, Chang Liu, Lijun Wu, Shufang Xie, Tong Wang, Yusong Wang, Wengang Zhou, Tao Qin, Houqiang Li, Tie-Yan Liu

In this work, we propose a method that directly predicts the coordinates of atoms.

Learning from Inside: Self-driven Siamese Sampling and Reasoning for Video Question Answering

no code implementations NeurIPS 2021 Weijiang Yu, Haoteng Zheng, Mengfei Li, Lei Ji, Lijun Wu, Nong Xiao, Nan Duan

To consider the interdependent knowledge between contextual clips into the network inference, we propose a Siamese Sampling and Reasoning (SiaSamRea) approach, which consists of a siamese sampling mechanism to generate sparse and similar clips (i. e., siamese clips) from the same video, and a novel reasoning strategy for integrating the interdependent knowledge between contextual clips into the network.

Question Answering Video Question Answering

How to Leverage Multimodal EHR Data for Better Medical Predictions?

1 code implementation29 Oct 2021 Bo Yang, Lijun Wu

Therefore, in this paper, we first extract the accompanying clinical notes from EHR and propose a method to integrate these data, we also comprehensively study the different models and the data leverage methods for better medical task prediction.

Pre-training Co-evolutionary Protein Representation via A Pairwise Masked Language Model

no code implementations29 Oct 2021 Liang He, Shizhuo Zhang, Lijun Wu, Huanhuan Xia, Fusong Ju, He Zhang, Siyuan Liu, Yingce Xia, Jianwei Zhu, Pan Deng, Bin Shao, Tao Qin, Tie-Yan Liu

The key problem in the protein sequence representation learning is to capture the co-evolutionary information reflected by the inter-residue co-variation in the sequences.

Language Modelling Multiple Sequence Alignment +1

Are BERT Families Zero-Shot Learners? A Study on Their Potential and Limitations

no code implementations29 Sep 2021 Yue Wang, Lijun Wu, Xiaobo Liang, Juntao Li, Min Zhang

Starting from the resurgence of deep learning, language models (LMs) have never been so popular.

DM-CT: Consistency Training with Data and Model Perturbation

no code implementations29 Sep 2021 Xiaobo Liang, Runze Mao, Lijun Wu, Juntao Li, Weiqing Liu, Qing Li, Min Zhang

The common approach of consistency training is performed on the data-level, which typically utilizes the data augmentation strategy (or adversarial training) to make the predictions from the augmented input and the original input to be consistent, so that the model is more robust and attains better generalization ability.

Data Augmentation Image Classification +2

Target-Side Data Augmentation for Sequence Generation

1 code implementation ICLR 2022 Shufang Xie, Ang Lv, Yingce Xia, Lijun Wu, Tao Qin, Rui Yan, Tie-Yan Liu

Autoregressive sequence generation, a prevalent task in machine learning and natural language processing, generates every target token conditioned on both a source input and previously generated target tokens.

Abstractive Text Summarization Data Augmentation +3

Discovering Drug-Target Interaction Knowledge from Biomedical Literature

no code implementations27 Sep 2021 Yutai Hou, Yingce Xia, Lijun Wu, Shufang Xie, Yang Fan, Jinhua Zhu, Wanxiang Che, Tao Qin, Tie-Yan Liu

We regard the DTI triplets as a sequence and use a Transformer-based model to directly generate them without using the detailed annotations of entities and relations.

Incorporating NODE with Pre-trained Neural Differential Operator for Learning Dynamics

no code implementations8 Jun 2021 Shiqi Gong, Qi Meng, Yue Wang, Lijun Wu, Wei Chen, Zhi-Ming Ma, Tie-Yan Liu

In this paper, to reduce the reliance on the numerical solver, we propose to enhance the supervised signal in the training of NODE.

Distance-Enhanced Graph Neural Network for Link Prediction

1 code implementation NA 2021 Boling Li, Yingce Xia, Shufang Xie, Lijun Wu, Tao Qin

To overcome this difficulty, we propose an anchorbased distance: First, we randomly select K anchor vertices from the graph and then calculate the shortest distances of all vertices in the graph to them.

Link Prediction Link Property Prediction

IOT: Instance-wise Layer Reordering for Transformer Structures

1 code implementation ICLR 2021 Jinhua Zhu, Lijun Wu, Yingce Xia, Shufang Xie, Tao Qin, Wengang Zhou, Houqiang Li, Tie-Yan Liu

Based on this observation, in this work, we break the assumption of the fixed layer order in the Transformer and introduce instance-wise layer reordering into the model structure.

Abstractive Text Summarization Code Generation +3

Learning to Use Future Information in Simultaneous Translation

1 code implementation1 Jan 2021 Xueqing Wu, Yingce Xia, Lijun Wu, Shufang Xie, Weiqing Liu, Tao Qin, Tie-Yan Liu

For wait-k inference, we observe that wait-m training with $m>k$ in simultaneous NMT (i. e., using more future information for training than inference) generally outperforms wait-k training.

Machine Translation Translation

ADD: Augmented Disentanglement Distillation Framework for Improving Stock Trend Forecasting

1 code implementation11 Dec 2020 Hongshun Tang, Lijun Wu, Weiqing Liu, Jiang Bian

Stock trend forecasting has become a popular research direction that attracts widespread attention in the financial field.

Disentanglement

Masked Contrastive Representation Learning for Reinforcement Learning

1 code implementation15 Oct 2020 Jinhua Zhu, Yingce Xia, Lijun Wu, Jiajun Deng, Wengang Zhou, Tao Qin, Houqiang Li

During inference, the CNN encoder and the policy network are used to take actions, and the Transformer module is discarded.

Atari Games Contrastive Learning +2

Temporally Correlated Task Scheduling for Sequence Learning

2 code implementations10 Jul 2020 Xueqing Wu, Lewen Wang, Yingce Xia, Weiqing Liu, Lijun Wu, Shufang Xie, Tao Qin, Tie-Yan Liu

In many applications, a sequence learning task is usually associated with multiple temporally correlated auxiliary tasks, which are different in terms of how much input information to use or which future step to predict.

Machine Translation Translation

Learning to Reweight with Deep Interactions

no code implementations9 Jul 2020 Yang Fan, Yingce Xia, Lijun Wu, Shufang Xie, Weiqing Liu, Jiang Bian, Tao Qin, Xiang-Yang Li

Recently, the concept of teaching has been introduced into machine learning, in which a teacher model is used to guide the training of a student model (which will be used in real tasks) through data selection, loss function design, etc.

Image Classification Machine Translation +1

Multi-branch Attentive Transformer

1 code implementation18 Jun 2020 Yang Fan, Shufang Xie, Yingce Xia, Lijun Wu, Tao Qin, Xiang-Yang Li, Tie-Yan Liu

While the multi-branch architecture is one of the key ingredients to the success of computer vision tasks, it has not been well investigated in natural language processing, especially sequence learning tasks.

Code Generation Machine Translation +3

Generalized Focal Loss: Learning Qualified and Distributed Bounding Boxes for Dense Object Detection

6 code implementations NeurIPS 2020 Xiang Li, Wenhai Wang, Lijun Wu, Shuo Chen, Xiaolin Hu, Jun Li, Jinhui Tang, Jian Yang

Specifically, we merge the quality estimation into the class prediction vector to form a joint representation of localization quality and classification, and use a vector to represent arbitrary distribution of box locations.

Classification Dense Object Detection +2

Incorporating BERT into Neural Machine Translation

3 code implementations ICLR 2020 Jinhua Zhu, Yingce Xia, Lijun Wu, Di He, Tao Qin, Wengang Zhou, Houqiang Li, Tie-Yan Liu

While BERT is more commonly used as fine-tuning instead of contextual embedding for downstream language understanding tasks, in NMT, our preliminary exploration of using BERT as contextual embedding is better than using for fine-tuning.

Natural Language Understanding Reading Comprehension +3

Exploiting Monolingual Data at Scale for Neural Machine Translation

no code implementations IJCNLP 2019 Lijun Wu, Yiren Wang, Yingce Xia, Tao Qin, Jian-Huang Lai, Tie-Yan Liu

In this work, we study how to use both the source-side and target-side monolingual data for NMT, and propose an effective strategy leveraging both of them.

 Ranked #1 on Machine Translation on WMT2016 English-German (SacreBLEU metric, using extra training data)

Machine Translation Translation

Machine Translation With Weakly Paired Documents

no code implementations IJCNLP 2019 Lijun Wu, Jinhua Zhu, Di He, Fei Gao, Tao Qin, Jian-Huang Lai, Tie-Yan Liu

1) We provide a simple approach to mine implicitly bilingual sentence pairs from document pairs which can then be used as supervised training signals.

Translation Unsupervised Machine Translation

Efficient Bidirectional Neural Machine Translation

no code implementations25 Aug 2019 Xu Tan, Yingce Xia, Lijun Wu, Tao Qin

In this paper, we propose an efficient method to generate a sequence in both left-to-right and right-to-left manners using a single encoder and decoder, combining the advantages of both generation directions.

Machine Translation Translation

Depth Growing for Neural Machine Translation

1 code implementation ACL 2019 Lijun Wu, Yiren Wang, Yingce Xia, Fei Tian, Fei Gao, Tao Qin, Jian-Huang Lai, Tie-Yan Liu

While very deep neural networks have shown effectiveness for computer vision and text classification applications, how to increase the network depth of neural machine translation (NMT) models for better translation quality remains a challenging problem.

Machine Translation Text Classification +1

Soft Contextual Data Augmentation for Neural Machine Translation

1 code implementation ACL 2019 Jinhua Zhu, Fei Gao, Lijun Wu, Yingce Xia, Tao Qin, Wengang Zhou, Xue-Qi Cheng, Tie-Yan Liu

While data augmentation is an important trick to boost the accuracy of deep learning methods in computer vision tasks, its study in natural language tasks is still very limited.

Data Augmentation Language Modelling +2

Learning to Teach with Dynamic Loss Functions

no code implementations NeurIPS 2018 Lijun Wu, Fei Tian, Yingce Xia, Yang Fan, Tao Qin, Jian-Huang Lai, Tie-Yan Liu

Different from typical learning settings in which the loss function of a machine learning model is predefined and fixed, in our framework, the loss function of a machine learning model (we call it student) is defined by another machine learning model (we call it teacher).

Image Classification Machine Translation

Beyond Error Propagation in Neural Machine Translation: Characteristics of Language Also Matter

no code implementations EMNLP 2018 Lijun Wu, Xu Tan, Di He, Fei Tian, Tao Qin, Jian-Huang Lai, Tie-Yan Liu

Many previous works have discussed the relationship between error propagation and the \emph{accuracy drop} (i. e., the left part of the translated sentence is often better than its right part in left-to-right decoding models) problem.

Machine Translation Text Summarization +1

A Study of Reinforcement Learning for Neural Machine Translation

1 code implementation EMNLP 2018 Lijun Wu, Fei Tian, Tao Qin, Jian-Huang Lai, Tie-Yan Liu

Recent studies have shown that reinforcement learning (RL) is an effective approach for improving the performance of neural machine translation (NMT) system.

Machine Translation reinforcement-learning +1

Efficient Sequence Learning with Group Recurrent Networks

no code implementations NAACL 2018 Fei Gao, Lijun Wu, Li Zhao, Tao Qin, Xue-Qi Cheng, Tie-Yan Liu

Recurrent neural networks have achieved state-of-the-art results in many artificial intelligence tasks, such as language modeling, neural machine translation, speech recognition and so on.

Machine Translation Speech Recognition +1

Deliberation Networks: Sequence Generation Beyond One-Pass Decoding

no code implementations NeurIPS 2017 Yingce Xia, Fei Tian, Lijun Wu, Jianxin Lin, Tao Qin, Nenghai Yu, Tie-Yan Liu

In this work, we introduce the deliberation process into the encoder-decoder framework and propose deliberation networks for sequence generation.

Image Captioning Machine Translation +2

Adversarial Neural Machine Translation

no code implementations20 Apr 2017 Lijun Wu, Yingce Xia, Li Zhao, Fei Tian, Tao Qin, Jian-Huang Lai, Tie-Yan Liu

The goal of the adversary is to differentiate the translation result generated by the NMT model from that by human.

Machine Translation Translation

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