21 citations · 87 across the 17 of their papers we have counts for
19 papers
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.…
Sense Embeddings are also Biased--Evaluating Social Biases in Static and Contextualised Sense Embeddings
Yi Zhou, Masahiro Kaneko, Danushka Bollegala
Sense embedding learning methods learn different embeddings for the different senses of an ambiguous word. One sense of an ambiguous word might be socially biased while its other s…
Interpretability for Language Learners Using Example-Based Grammatical Error Correction
Masahiro Kaneko, Sho Takase, Ayana Niwa +1
Grammatical Error Correction (GEC) should not focus only on high accuracy of corrections but also on interpretability for language learning. However, existing neural-based GEC mode…
Proficiency Matters Quality Estimation in Grammatical Error Correction
Yujin Takahashi, Masahiro Kaneko, Masato Mita +1
This study investigates how supervised quality estimation (QE) models of grammatical error correction (GEC) are affected by the learners' proficiency with the data. QE models for G…
ExtraPhrase: Efficient Data Augmentation for Abstractive Summarization
Mengsay Loem, Sho Takase, Masahiro Kaneko +1
Neural models trained with large amount of parallel data have achieved impressive performance in abstractive summarization tasks. However, large-scale parallel corpora are expensiv…