94 citations · 130 across the 17 of their papers we have counts for
17 papers
Multi-perspective Alignment for Increasing Naturalness in Neural Machine Translation
Huiyuan Lai, Esther Ploeger, Rik van Noord +1
Neural machine translation (NMT) systems amplify lexical biases present in their training data, leading to artificially impoverished language in output translations. These language…
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…
Do Language Models Care About Text Quality? Evaluating Web-Crawled Corpora Across 11 Languages
Rik van Noord, Taja Kuzman, Peter Rupnik +4
Large, curated, web-crawled corpora play a vital role in training language models (LMs). They form the lion's share of the training data in virtually all recent LMs, such as the we…
To be or not to be: a translation reception study of a literary text translated into Dutch and Catalan using machine translation
Ana Guerberof Arenas, Antonio Toral
This article presents the results of a study involving the reception of a fictional story by Kurt Vonnegut translated from English into Catalan and Dutch in three conditions: machi…
Multilingual Multi-Figurative Language Detection
Huiyuan Lai, Antonio Toral, Malvina Nissim
Figures of speech help people express abstract concepts and evoke stronger emotions than literal expressions, thereby making texts more creative and engaging. Due to its pervasive…
Missing Information, Unresponsive Authors, Experimental Flaws: The Impossibility of Assessing the Reproducibility of Previous Human Evaluations in NLP
Anya Belz, Craig Thomson, Ehud Reiter +39
We report our efforts in identifying a set of previous human evaluations in NLP that would be suitable for a coordinated study examining what makes human evaluations in NLP more/le…
Multidimensional Evaluation for Text Style Transfer Using ChatGPT
Huiyuan Lai, Antonio Toral, Malvina Nissim
We investigate the potential of ChatGPT as a multidimensional evaluator for the task of \emph{Text Style Transfer}, alongside, and in comparison to, existing automatic metrics as w…
Are Character-level Translations Worth the Wait? Comparing ByT5 and mT5 for Machine Translation
Lukas Edman, Gabriele Sarti, Antonio Toral +2
Pretrained character-level and byte-level language models have been shown to be competitive with popular subword models across a range of Natural Language Processing (NLP) tasks. H…
Subword-Delimited Downsampling for Better Character-Level Translation
Lukas Edman, Antonio Toral, Gertjan van Noord
Subword-level models have been the dominant paradigm in NLP. However, character-level models have the benefit of seeing each character individually, providing the model with more d…
Patching Leaks in the Charformer for Efficient Character-Level Generation
Lukas Edman, Antonio Toral, Gertjan van Noord
Character-based representations have important advantages over subword-based ones for morphologically rich languages. They come with increased robustness to noisy input and do not…
The Importance of Context in Very Low Resource Language Modeling
Lukas Edman, Antonio Toral, Gertjan van Noord
This paper investigates very low resource language model pretraining, when less than 100 thousand sentences are available. We find that, in very low resource scenarios, statistical…
DivEMT: Neural Machine Translation Post-Editing Effort Across Typologically Diverse Languages
Gabriele Sarti, Arianna Bisazza, Ana Guerberof Arenas +1
We introduce DivEMT, the first publicly available post-editing study of Neural Machine Translation (NMT) over a typologically diverse set of target languages. Using a strictly cont…
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…
Creativity in translation: machine translation as a constraint for literary texts
Ana Guerberof Arenas, Antonio Toral
This article presents the results of a study involving the translation of a short story by Kurt Vonnegut from English to Catalan and Dutch using three modalities: machine-translati…
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…