26 citations · 74 across the 47 of their papers we have counts for
9 papers · 2 filters
A Predictive Factor Analysis of Social Biases and Task-Performance in Pretrained Masked Language Models
Yi Zhou, Jose Camacho-Collados, Danushka Bollegala
Various types of social biases have been reported with pretrained Masked Language Models (MLMs) in prior work. However, multiple underlying factors are associated with an MLM such…
Can Word Sense Distribution Detect Semantic Changes of Words?
Xiaohang Tang, Yi Zhou, Taichi Aida +2
Semantic Change Detection (SCD) of words is an important task for various NLP applications that must make time-sensitive predictions. Some words are used over time in novel ways to…
-- Predicting the Semantic Changes in Words across Corpora by Context Swapping
Taichi Aida, Danushka Bollegala
Meanings of words change over time and across domains. Detecting the semantic changes of words is an important task for various NLP applications that must make time-sensitive predi…
The Impact of Debiasing on the Performance of Language Models in Downstream Tasks is Underestimated
Masahiro Kaneko, Danushka Bollegala, Naoaki Okazaki
Pre-trained language models trained on large-scale data have learned serious levels of social biases. Consequently, various methods have been proposed to debias pre-trained models.…
In-Contextual Gender Bias Suppression for Large Language Models
Daisuke Oba, Masahiro Kaneko, Danushka Bollegala
Despite their impressive performance in a wide range of NLP tasks, Large Language Models (LLMs) have been reported to encode worrying-levels of gender biases. Prior work has propos…
Together We Make Sense -- Learning Meta-Sense Embeddings from Pretrained Static Sense Embeddings
Haochen Luo, Yi Zhou, Danushka Bollegala
Sense embedding learning methods learn multiple vectors for a given ambiguous word, corresponding to its different word senses. For this purpose, different methods have been propos…