4 citations · 5 across the 6 of their papers we have counts for
7 papers
Towards Tailored Recovery of Lexical Diversity in Literary Machine Translation
Esther Ploeger, Huiyuan Lai, Rik van Noord +1
Machine translations are found to be lexically poorer than human translations. The loss of lexical diversity through MT poses an issue in the automatic translation of literature, w…
Multi-Figurative Language Generation
Huiyuan Lai, Malvina Nissim
Figurative language generation is the task of reformulating a given text in the desired figure of speech while still being faithful to the original context. We take the first step…
Human Judgement as a Compass to Navigate Automatic Metrics for Formality Transfer
Huiyuan Lai, Jiali Mao, Antonio Toral +1
Although text style transfer has witnessed rapid development in recent years, there is as yet no established standard for evaluation, which is performed using several automatic met…
Multilingual Pre-training with Language and Task Adaptation for Multilingual Text Style Transfer
Huiyuan Lai, Antonio Toral, Malvina Nissim
We exploit the pre-trained seq2seq model mBART for multilingual text style transfer. Using machine translated data as well as gold aligned English sentences yields state-of-the-art…
Generic resources are what you need: Style transfer tasks without task-specific parallel training data
Huiyuan Lai, Antonio Toral, Malvina Nissim
Style transfer aims to rewrite a source text in a different target style while preserving its content. We propose a novel approach to this task that leverages generic resources, an…
Thank you BART! Rewarding Pre-Trained Models Improves Formality Style Transfer
Huiyuan Lai, Antonio Toral, Malvina Nissim
Scarcity of parallel data causes formality style transfer models to have scarce success in preserving content. We show that fine-tuning pre-trained language (GPT-2) and sequence-to…