Search Results for author: Yingce Xia

Found 61 papers, 33 papers with code

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

FABind: Fast and Accurate Protein-Ligand Binding

1 code implementation NeurIPS 2023 Qizhi Pei, Kaiyuan Gao, Lijun Wu, Jinhua Zhu, Yingce Xia, Shufang Xie, Tao Qin, Kun He, Tie-Yan Liu, Rui Yan

In this work, we propose $\mathbf{FABind}$, an end-to-end model that combines pocket prediction and docking to achieve accurate and fast protein-ligand binding.

Drug Discovery Pose Estimation +1

Retrosynthesis Prediction with Local Template Retrieval

no code implementations7 Jun 2023 Shufang Xie, Rui Yan, Junliang Guo, Yingce Xia, Lijun Wu, Tao Qin

Furthermore, we propose a lightweight adapter to adjust the weights when combing neural network and KNN predictions conditioned on the hidden representation and the retrieved templates.

Drug Discovery Retrieval +1

MolXPT: Wrapping Molecules with Text for Generative Pre-training

no code implementations18 May 2023 Zequn Liu, Wei zhang, Yingce Xia, Lijun Wu, Shufang Xie, Tao Qin, Ming Zhang, Tie-Yan Liu

Considering that text is the most important record for scientific discovery, in this paper, we propose MolXPT, a unified language model of text and molecules pre-trained on SMILES (a sequence representation of molecules) wrapped by text.

Language Modelling Molecular Property Prediction +3

What are the Desired Characteristics of Calibration Sets? Identifying Correlates on Long Form Scientific Summarization

1 code implementation12 May 2023 Griffin Adams, Bichlien H Nguyen, Jake Smith, Yingce Xia, Shufang Xie, Anna Ostropolets, Budhaditya Deb, Yuan-Jyue Chen, Tristan Naumann, Noémie Elhadad

Summarization models often generate text that is poorly calibrated to quality metrics because they are trained to maximize the likelihood of a single reference (MLE).

O-GNN: Incorporating Ring Priors into Molecular Modeling

1 code implementation ICLR 2023 Jinhua Zhu, Kehan Wu, Bohan Wang, Yingce Xia, Shufang Xie, Qi Meng, Lijun Wu, Tao Qin, Wengang Zhou, Houqiang Li, Tie-Yan Liu

Despite the recent success of molecular modeling with graph neural networks (GNNs), few models explicitly take rings in compounds into consideration, consequently limiting the expressiveness of the models.

 Ranked #1 on Graph Regression on PCQM4M-LSC (Validation MAE metric)

Graph Regression Molecular Property Prediction +3

De Novo Molecular Generation via Connection-aware Motif Mining

1 code implementation2 Feb 2023 Zijie Geng, Shufang Xie, Yingce Xia, Lijun Wu, Tao Qin, Jie Wang, Yongdong Zhang, Feng Wu, Tie-Yan Liu

The obtained motif vocabulary consists of not only molecular motifs (i. e., the frequent fragments), but also their connection information, indicating how the motifs are connected with each other.

Incorporating Pre-training Paradigm for Antibody Sequence-Structure Co-design

no code implementations26 Oct 2022 Kaiyuan Gao, Lijun Wu, Jinhua Zhu, Tianbo Peng, Yingce Xia, Liang He, Shufang Xie, Tao Qin, Haiguang Liu, Kun He, Tie-Yan Liu

Specifically, we first pre-train an antibody language model based on the sequence data, then propose a one-shot way for sequence and structure generation of CDR to avoid the heavy cost and error propagation from an autoregressive manner, and finally leverage the pre-trained antibody model for the antigen-specific antibody generation model with some carefully designed modules.

Language Modelling Specificity

BioGPT: Generative Pre-trained Transformer for Biomedical Text Generation and Mining

2 code implementations19 Oct 2022 Renqian Luo, Liai Sun, Yingce Xia, Tao Qin, Sheng Zhang, Hoifung Poon, Tie-Yan Liu

Pre-trained language models have attracted increasing attention in the biomedical domain, inspired by their great success in the general natural language domain.

 Ranked #1 on Document Classification on HOC (Micro F1 metric)

Document Classification Language Modelling +3

Unified 2D and 3D Pre-Training of Molecular Representations

1 code implementation14 Jul 2022 Jinhua Zhu, Yingce Xia, Lijun Wu, Shufang Xie, Tao Qin, Wengang Zhou, Houqiang Li, Tie-Yan Liu

The model is pre-trained on three tasks: reconstruction of masked atoms and coordinates, 3D conformation generation conditioned on 2D graph, and 2D graph generation conditioned on 3D conformation.

Graph Generation Molecular Property Prediction +3

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 Multi-step retrosynthesis

SSM-DTA: Breaking the Barriers of Data Scarcity in Drug-Target Affinity Prediction

2 code implementations20 Jun 2022 Qizhi Pei, Lijun Wu, Jinhua Zhu, Yingce Xia, Shufang Xie, Tao Qin, Haiguang Liu, Tie-Yan Liu, Rui Yan

Accurate prediction of Drug-Target Affinity (DTA) is of vital importance in early-stage drug discovery, facilitating the identification of drugs that can effectively interact with specific targets and regulate their activities.

Drug Discovery Language Modelling +2

Direct Molecular Conformation Generation

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

Molecular conformation generation aims to generate three-dimensional coordinates of all the atoms in a molecule and is an important task in bioinformatics and pharmacology.

Molecular Docking

DDG-DA: Data Distribution Generation for Predictable Concept Drift Adaptation

1 code implementation11 Jan 2022 Wendi Li, Xiao Yang, Weiqing Liu, Yingce Xia, Jiang Bian

To handle concept drift, previous methods first detect when/where the concept drift happens and then adapt models to fit the distribution of the latest data.

Stock Prediction

SHGNN: Structure-Aware Heterogeneous Graph Neural Network

1 code implementation12 Dec 2021 Wentao Xu, Yingce Xia, Weiqing Liu, Jiang Bian, Jian Yin, Tie-Yan Liu

Next, we use a tree-attention aggregator to incorporate the graph structure information into the aggregation module on the meta-path.

Graph Embedding Node Classification

Stylized Dialogue Generation with Multi-Pass Dual Learning

1 code implementation NeurIPS 2021 Jinpeng Li, Yingce Xia, Rui Yan, Hongda Sun, Dongyan Zhao, Tie-Yan Liu

Considering there is no parallel data between the contexts and the responses of target style S1, existing works mainly use back translation to generate stylized synthetic data for training, where the data about context, target style S1 and an intermediate style S0 is used.

Dialogue Generation

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

HIST: A Graph-based Framework for Stock Trend Forecasting via Mining Concept-Oriented Shared Information

2 code implementations26 Oct 2021 Wentao Xu, Weiqing Liu, Lewen Wang, Yingce Xia, Jiang Bian, Jian Yin, Tie-Yan Liu

To overcome the shortcomings of previous work, we proposed a novel stock trend forecasting framework that can adequately mine the concept-oriented shared information from predefined concepts and hidden concepts.

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 +2

MS$^2$-Transformer: An End-to-End Model for MS/MS-assisted Molecule Identification

no code implementations29 Sep 2021 Mengji Zhang, Yingce Xia, Nian Wu, Kun Qian, Jianyang Zeng

Manually interpreting the MS/MS spectrum into the molecules (i. e., the simplified molecular-input line-entry system, SMILES) is often costly and cumbersome, mainly due to the synthesis and labeling of isotopes and the requirement of expert knowledge.

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.

Dual-view Molecule Pre-training

1 code implementation17 Jun 2021 Jinhua Zhu, Yingce Xia, Tao Qin, Wengang Zhou, Houqiang Li, Tie-Yan Liu

After pre-training, we can use either the Transformer branch (this one is recommended according to empirical results), the GNN branch, or both for downstream tasks.

Molecular Property Prediction Property Prediction +2

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 +2

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 NMT +1

COSEA: Convolutional Code Search with Layer-wise Attention

no code implementations19 Oct 2020 Hao Wang, Jia Zhang, Yingce Xia, Jiang Bian, Chao Zhang, Tie-Yan Liu

However, most existing studies overlook the code's intrinsic structural logic, which indeed contains a wealth of semantic information, and fails to capture intrinsic features of codes.

Code Search

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 +3

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 Scheduling +1

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 +2

Dual Learning: Theoretical Study and an Algorithmic Extension

no code implementations17 May 2020 Zhibing Zhao, Yingce Xia, Tao Qin, Lirong Xia, Tie-Yan Liu

Dual learning has been successfully applied in many machine learning applications including machine translation, image-to-image transformation, etc.

Machine Translation Translation

TuiGAN: Learning Versatile Image-to-Image Translation with Two Unpaired Images

1 code implementation ECCV 2020 Jianxin Lin, Yingxue Pang, Yingce Xia, Zhibo Chen, Jiebo Luo

With TuiGAN, an image is translated in a coarse-to-fine manner where the generated image is gradually refined from global structures to local details.

Translation Unsupervised Image-To-Image Translation +1

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 NMT +4

Neural Machine Translation with Soft Prototype

1 code implementation NeurIPS 2019 Yiren Wang, Yingce Xia, Fei Tian, Fei Gao, Tao Qin, Cheng Xiang Zhai, Tie-Yan Liu

Neural machine translation models usually use the encoder-decoder framework and generate translation from left to right (or right to left) without fully utilizing the target-side global information.

Machine Translation Translation

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 NMT +1

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

Multilingual Neural Machine Translation with Language Clustering

no code implementations IJCNLP 2019 Xu Tan, Jiale Chen, Di He, Yingce Xia, Tao Qin, Tie-Yan Liu

We study two methods for language clustering: (1) using prior knowledge, where we cluster languages according to language family, and (2) using language embedding, in which we represent each language by an embedding vector and cluster them in the embedding space.

Clustering Machine Translation +2

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 NMT +3

Image-to-Image Translation with Multi-Path Consistency Regularization

no code implementations29 May 2019 Jianxin Lin, Yingce Xia, Yijun Wang, Tao Qin, Zhibo Chen

In this work, we introduce a new kind of loss, multi-path consistency loss, which evaluates the differences between direct translation $\mathcal{D}_s\to\mathcal{D}_t$ and indirect translation $\mathcal{D}_s\to\mathcal{D}_a\to\mathcal{D}_t$ with $\mathcal{D}_a$ as an auxiliary domain, to regularize training.

Face to Face Translation Image-to-Image Translation +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

Multi-Agent Dual Learning

no code implementations ICLR 2019 Yiren Wang, Yingce Xia, Tianyu He, Fei Tian, Tao Qin, ChengXiang Zhai, Tie-Yan Liu

Dual learning has attracted much attention in machine learning, computer vision and natural language processing communities.

Machine Translation Translation

Dual Learning: Theoretical Study and Algorithmic Extensions

no code implementations ICLR 2019 Zhibing Zhao, Yingce Xia, Tao Qin, Tie-Yan Liu

Based on the theoretical discoveries, we extend dual learning by introducing more related mappings and propose highly symmetric frameworks, cycle dual learning and multipath dual learning, in both of which we can leverage the feedback signals from additional domains to improve the qualities of the mappings.

Machine Translation Translation

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).

BIG-bench Machine Learning Image Classification +1

Model-Level Dual Learning

no code implementations ICML 2018 Yingce Xia, Xu Tan, Fei Tian, Tao Qin, Nenghai Yu, Tie-Yan Liu

Many artificial intelligence tasks appear in dual forms like English$\leftrightarrow$French translation and speech$\leftrightarrow$text transformation.

Machine Translation Sentiment Analysis +1

Conditional Image-to-Image Translation

no code implementations CVPR 2018 Jianxin Lin, Yingce Xia, Tao Qin, Zhibo Chen, Tie-Yan Liu

In this paper, we study a new problem, conditional image-to-image translation, which is to translate an image from the source domain to the target domain conditioned on a given image in the target domain.

Image-to-Image Translation Translation

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

Decoding with Value Networks for Neural Machine Translation

no code implementations NeurIPS 2017 Di He, Hanqing Lu, Yingce Xia, Tao Qin, Li-Wei Wang, Tie-Yan Liu

Inspired by the success and methodology of AlphaGo, in this paper we propose using a prediction network to improve beam search, which takes the source sentence $x$, the currently available decoding output $y_1,\cdots, y_{t-1}$ and a candidate word $w$ at step $t$ as inputs and predicts the long-term value (e. g., BLEU score) of the partial target sentence if it is completed by the NMT model.

Machine Translation NMT +2

Dual Supervised Learning

1 code implementation ICML 2017 Yingce Xia, Tao Qin, Wei Chen, Jiang Bian, Nenghai Yu, Tie-Yan Liu

Many supervised learning tasks are emerged in dual forms, e. g., English-to-French translation vs. French-to-English translation, speech recognition vs. text to speech, and image classification vs. image generation.

General Classification Image Classification +6

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 NMT +1

Dual Learning for Machine Translation

1 code implementation NeurIPS 2016 Yingce Xia, Di He, Tao Qin, Li-Wei Wang, Nenghai Yu, Tie-Yan Liu, Wei-Ying Ma

Based on the feedback signals generated during this process (e. g., the language-model likelihood of the output of a model, and the reconstruction error of the original sentence after the primal and dual translations), we can iteratively update the two models until convergence (e. g., using the policy gradient methods).

Language Modelling Machine Translation +3

Thompson Sampling for Budgeted Multi-armed Bandits

no code implementations1 May 2015 Yingce Xia, Haifang Li, Tao Qin, Nenghai Yu, Tie-Yan Liu

In this paper, we extend the Thompson sampling to Budgeted MAB, where there is random cost for pulling an arm and the total cost is constrained by a budget.

Multi-Armed Bandits Thompson Sampling

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