activity
20182021
most citedA Cross-Domain Transferable Neural Coherence Model

5 citations · 8 across the 3 of their papers we have counts for

collaborators

8 papers

cs.CL2021

Turing: an Accurate and Interpretable Multi-Hypothesis Cross-Domain Natural Language Database Interface

Peng Xu, Wenjie Zi, Hamidreza Shahidi +7

A natural language database interface (NLDB) can democratize data-driven insights for non-technical users. However, existing Text-to-SQL semantic parsers cannot achieve high enough…

cs.CL2020

Optimizing Deeper Transformers on Small Datasets

Peng Xu, Dhruv Kumar, Wei Yang +6

It is a common belief that training deep transformers from scratch requires large datasets. Consequently, for small datasets, people usually use shallow and simple additional layer…

cs.LG2019

Better Long-Range Dependency By Bootstrapping A Mutual Information Regularizer

Yanshuai Cao, Peng Xu

In this work, we develop a novel regularizer to improve the learning of long-range dependency of sequence data. Applied on language modelling, our regularizer expresses the inducti…

cs.CL2019

On Variational Learning of Controllable Representations for Text without Supervision

Peng Xu, Jackie Chi Kit Cheung, Yanshuai Cao

The variational autoencoder (VAE) can learn the manifold of natural images on certain datasets, as evidenced by meaningful interpolating or extrapolating in the continuous latent s…

cs.CL20195 cited

A Cross-Domain Transferable Neural Coherence Model

Peng Xu, Hamidreza Saghir, Jin Sung Kang +4

Coherence is an important aspect of text quality and is crucial for ensuring its readability. One important limitation of existing coherence models is that training on one domain d…

cs.CL20193 cited

Connecting Language and Knowledge with Heterogeneous Representations for Neural Relation Extraction

Peng Xu, Denilson Barbosa

Knowledge Bases (KBs) require constant up-dating to reflect changes to the world they represent. For general purpose KBs, this is often done through Relation Extraction (RE), the t…