Search Results for author: Pai Liu

Found 8 papers, 3 papers with code

Assign Experiment Variants at Scale in Online Controlled Experiments

no code implementations17 Dec 2022 Qike Li, Samir Jamkhande, Pavel Kochetkov, Pai Liu

The randomized assignment maps end users to experiment buckets and balances user characteristics between the groups.

Open Information Extraction from 2007 to 2022 -- A Survey

no code implementations18 Aug 2022 Pai Liu, Wenyang Gao, Wenjie Dong, Songfang Huang, Yue Zhang

Open information extraction is an important NLP task that targets extracting structured information from unstructured text without limitations on the relation type or the domain of the text.

Open Information Extraction

Exploring Generative Neural Temporal Point Process

1 code implementation3 Aug 2022 Haitao Lin, Lirong Wu, Guojiang Zhao, Pai Liu, Stan Z. Li

While lots of previous works have focused on `goodness-of-fit' of TPP models by maximizing the likelihood, their predictive performance is unsatisfactory, which means the timestamps generated by models are far apart from true observations.

Denoising

Can Generative Pre-trained Language Models Serve as Knowledge Bases for Closed-book QA?

1 code implementation ACL 2021 Cunxiang Wang, Pai Liu, Yue Zhang

Recent work has investigated the interesting question using pre-trained language models (PLMs) as knowledge bases for answering open questions.

Question Answering

QiaoNing at SemEval-2020 Task 4: Commonsense Validation and Explanation system based on ensemble of language model

no code implementations SEMEVAL 2020 Pai Liu

In this paper, we present language model system submitted to SemEval-2020 Task 4 competition: "Commonsense Validation and Explanation".

Language Modelling Transfer Learning

Introducing a Generative Adversarial Network Model for Lagrangian Trajectory Simulation

no code implementations13 Jan 2019 Jingwei Gan, Pai Liu, Rajan K. Chakrabarty

We introduce a generative adversarial network (GAN) model to simulate the 3-dimensional Lagrangian motion of particles trapped in the recirculation zone of a buoyancy-opposed flame.

Variational Autoencoding the Lagrangian Trajectories of Particles in a Combustion System

1 code implementation29 Nov 2018 Pai Liu, Jingwei Gan, Rajan K. Chakrabarty

We introduce a deep learning method to simulate the motion of particles trapped in a chaotic recirculating flame.

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