15 citations · 23 across the 5 of their papers we have counts for
5 papers
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.…
From Masked Language Modeling to Translation: Non-English Auxiliary Tasks Improve Zero-shot Spoken Language Understanding
Rob van der Goot, Ibrahim Sharaf, Aizhan Imankulova +6
The lack of publicly available evaluation data for low-resource languages limits progress in Spoken Language Understanding (SLU). As key tasks like intent classification and slot f…
Simultaneous Multi-Pivot Neural Machine Translation
Raj Dabre, Aizhan Imankulova, Masahiro Kaneko +1
Parallel corpora are indispensable for training neural machine translation (NMT) models, and parallel corpora for most language pairs do not exist or are scarce. In such cases, piv…
Towards Multimodal Simultaneous Neural Machine Translation
Aizhan Imankulova, Masahiro Kaneko, Tosho Hirasawa +1
Simultaneous translation involves translating a sentence before the speaker's utterance is completed in order to realize real-time understanding in multiple languages. This task is…
Exploiting Out-of-Domain Parallel Data through Multilingual Transfer Learning for Low-Resource Neural Machine Translation
Aizhan Imankulova, Raj Dabre, Atsushi Fujita +1
This paper proposes a novel multilingual multistage fine-tuning approach for low-resource neural machine translation (NMT), taking a challenging Japanese--Russian pair for benchmar…