20 citations · 30 across the 8 of their papers we have counts for
13 papers
On the Curious Case of norm of Sense Embeddings
Yi Zhou, Danushka Bollegala
We show that the norm of a static sense embedding encodes information related to the frequency of that sense in the training corpus used to learn the sense embeddings. Thi…
Debiasing isn't enough! -- On the Effectiveness of Debiasing MLMs and their Social Biases in Downstream Tasks
Masahiro Kaneko, Danushka Bollegala, Naoaki Okazaki
We study the relationship between task-agnostic intrinsic and task-specific extrinsic social bias evaluation measures for Masked Language Models (MLMs), and find that there exists…
Gender Bias in Masked Language Models for Multiple Languages
Masahiro Kaneko, Aizhan Imankulova, Danushka Bollegala +1
Masked Language Models (MLMs) pre-trained by predicting masked tokens on large corpora have been used successfully in natural language processing tasks for a variety of languages.…
A Survey on Word Meta-Embedding Learning
Danushka Bollegala, James O'Neill
Meta-embedding (ME) learning is an emerging approach that attempts to learn more accurate word embeddings given existing (source) word embeddings as the sole input. Due to their ab…
Unsupervised Attention-based Sentence-Level Meta-Embeddings from Contextualised Language Models
Keigo Takahashi, Danushka Bollegala
A variety of contextualised language models have been proposed in the NLP community, which are trained on diverse corpora to produce numerous Neural Language Models (NLMs). However…
Position-based Prompting for Health Outcome Generation
M. Abaho, D. Bollegala, P. Williamson +1
Probing Pre-trained Language Models (PLMs) using prompts has indirectly implied that language models (LMs) can be treated as knowledge bases. To this end, this phenomena has been e…