Search Results for author: Zequn Liu

Found 10 papers, 4 papers with code

Pathway2Text: Dataset and Method for Biomedical Pathway Description Generation

1 code implementation Findings (NAACL) 2022 Junwei Yang, Zequn Liu, Ming Zhang, Sheng Wang

Collectively, we envision our method will become an important benchmark for evaluating Graph2Text methods and advance biomedical research for complex diseases.

named-entity-recognition Named Entity Recognition +2

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

A Comprehensive Survey on Deep Graph Representation Learning

no code implementations11 Apr 2023 Wei Ju, Zheng Fang, Yiyang Gu, Zequn Liu, Qingqing Long, Ziyue Qiao, Yifang Qin, Jianhao Shen, Fang Sun, Zhiping Xiao, Junwei Yang, Jingyang Yuan, Yusheng Zhao, Yifan Wang, Xiao Luo, Ming Zhang

Graph representation learning aims to effectively encode high-dimensional sparse graph-structured data into low-dimensional dense vectors, which is a fundamental task that has been widely studied in a range of fields, including machine learning and data mining.

Graph Embedding Graph Neural Network +1

Robust Dancer: Long-term 3D Dance Synthesis Using Unpaired Data

no code implementations29 Mar 2023 Bin Feng, Tenglong Ao, Zequn Liu, Wei Ju, Libin Liu, Ming Zhang

How to automatically synthesize natural-looking dance movements based on a piece of music is an incrementally popular yet challenging task.


When does MAML Work the Best? An Empirical Study on Model-Agnostic Meta-Learning in NLP Applications

no code implementations24 May 2020 Zequn Liu, Ruiyi Zhang, Yiping Song, Wei Ju, Ming Zhang

Model-Agnostic Meta-Learning (MAML), a model-agnostic meta-learning method, is successfully employed in NLP applications including few-shot text classification and multi-domain low-resource language generation.

Few-Shot Text Classification Language Modelling +3

Learning to Customize Model Structures for Few-shot Dialogue Generation Tasks

1 code implementation ACL 2020 Yiping Song, Zequn Liu, Wei Bi, Rui Yan, Ming Zhang

Training the generative models with minimal corpus is one of the critical challenges for building open-domain dialogue systems.

Dialogue Generation Diversity +2

Early Prediction of Sepsis From Clinical Datavia Heterogeneous Event Aggregation

no code implementations14 Oct 2019 Lu-chen Liu, Haoxian Wu, Zichang Wang, Zequn Liu, Ming Zhang

Rather than directly applying the LSTM model to the event sequences, our proposed model firstly aggregates heterogeneous clinical events in a short period and then captures temporal interactions of the aggregated representations with LSTM.

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