1 citations · 1 across the 3 of their papers we have counts for
6 papers
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…
COMETA: A Corpus for Medical Entity Linking in the Social Media
Marco Basaldella, Fangyu Liu, Ehsan Shareghi +1
Whilst there has been growing progress in Entity Linking (EL) for general language, existing datasets fail to address the complex nature of health terminology in layman's language.…
Self-Alignment Pretraining for Biomedical Entity Representations
Fangyu Liu, Ehsan Shareghi, Zaiqiao Meng +2
Despite the widespread success of self-supervised learning via masked language models (MLM), accurately capturing fine-grained semantic relationships in the biomedical domain remai…
Learning Sparse Sentence Encoding without Supervision: An Exploration of Sparsity in Variational Autoencoders
Victor Prokhorov, Yingzhen Li, Ehsan Shareghi +1
It has been long known that sparsity is an effective inductive bias for learning efficient representation of data in vectors with fixed dimensionality, and it has been explored in…
On the Importance of the Kullback-Leibler Divergence Term in Variational Autoencoders for Text Generation
Victor Prokhorov, Ehsan Shareghi, Yingzhen Li +2
Variational Autoencoders (VAEs) are known to suffer from learning uninformative latent representation of the input due to issues such as approximated posterior collapse, or entangl…
Structured Prediction of Sequences and Trees using Infinite Contexts
Ehsan Shareghi, Gholamreza Haffari, Trevor Cohn +1
Linguistic structures exhibit a rich array of global phenomena, however commonly used Markov models are unable to adequately describe these phenomena due to their strong locality a…