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20152021
most citedMixture-of-Partitions: Infusing Large Biomedical Knowledge Graphs into BERT

1 citations · 3 across the 7 of their papers we have counts for

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Showing 2021Show all

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cs.LG2021

The Neglected Sibling: Isotropic Gaussian Posterior for VAE

Lan Zhang, Wray Buntine, Ehsan Shareghi

Deep generative models have been widely used in several areas of NLP, and various techniques have been proposed to augment them or address their training challenges. In this paper,…

cs.CL2021

It is Not as Good as You Think! Evaluating Simultaneous Machine Translation on Interpretation Data

Jinming Zhao, Philip Arthur, Gholamreza Haffari +2

Most existing simultaneous machine translation (SiMT) systems are trained and evaluated on offline translation corpora. We argue that SiMT systems should be trained and tested on r…

cs.CL20211 cited

Mixture-of-Partitions: Infusing Large Biomedical Knowledge Graphs into BERT

Zaiqiao Meng, Fangyu Liu, Thomas Hikaru Clark +2

Infusing factual knowledge into pre-trained models is fundamental for many knowledge-intensive tasks. In this paper, we proposed Mixture-of-Partitions (MoP), an infusion approach t…

cs.CL20211 cited

Unsupervised Representation Disentanglement of Text: An Evaluation on Synthetic Datasets

Lan Zhang, Victor Prokhorov, Ehsan Shareghi

To highlight the challenges of achieving representation disentanglement for text domain in an unsupervised setting, in this paper we select a representative set of successfully app…

cs.CL2021

Combining Deep Generative Models and Multi-lingual Pretraining for Semi-supervised Document Classification

Yi Zhu, Ehsan Shareghi, Yingzhen Li +2

Semi-supervised learning through deep generative models and multi-lingual pretraining techniques have orchestrated tremendous success across different areas of NLP. Nonetheless, th…