40 citations · 152 across the 36 of their papers we have counts for
6 papers · 1 filter
Learning Sense-Specific Static Embeddings using Contextualised Word Embeddings as a Proxy
Yi Zhou, Danushka Bollegala
Contextualised word embeddings generated from Neural Language Models (NLMs), such as BERT, represent a word with a vector that considers the semantics of the target word as well it…
Semantically-Conditioned Negative Samples for Efficient Contrastive Learning
James O' Neill, Danushka Bollegala
Negative sampling is a limiting factor w.r.t. the generalization of metric-learned neural networks. We show that uniform negative sampling provides little information about the cla…
RelWalk A Latent Variable Model Approach to Knowledge Graph Embedding
Danushka Bollegala, Huda Hakami, Yuichi Yoshida +1
Embedding entities and relations of a knowledge graph in a low-dimensional space has shown impressive performance in predicting missing links between entities. Although progresses…
Dictionary-based Debiasing of Pre-trained Word Embeddings
Masahiro Kaneko, Danushka Bollegala
Word embeddings trained on large corpora have shown to encode high levels of unfair discriminatory gender, racial, religious and ethnic biases. In contrast, human-written dictionar…
Debiasing Pre-trained Contextualised Embeddings
Masahiro Kaneko, Danushka Bollegala
In comparison to the numerous debiasing methods proposed for the static non-contextualised word embeddings, the discriminative biases in contextualised embeddings have received rel…
-Neighbor Based Curriculum Sampling for Sequence Prediction
James O' Neill, Danushka Bollegala
Multi-step ahead prediction in language models is challenging due to the discrepancy between training and test time processes. At test time, a sequence predictor is required to mak…