Search Results for author:
Found 16 papers, 11 papers with code
Human Perception in Natural Language Generation
We ask subjects whether they perceive as human-produced a bunch of texts, some of which are actually human-written, while others are automatically generated.
Multi-perspective Alignment for Increasing Naturalness in Neural Machine Translation
Neural machine translation (NMT) systems amplify lexical biases present in their training data, leading to artificially impoverished language in output translations.
Towards Tailored Recovery of Lexical Diversity in Literary Machine Translation
Thus, rather than aiming for the rigid increase of lexical diversity, we reframe the task as recovering what is lost in the machine translation process.
Fine-tuning with HED-IT: The impact of human post-editing for dialogical language models
Results from both human and automatic evaluation show that the different quality of training data is clearly perceived and it has an impact also on the models trained on such data.
mCoT: Multilingual Instruction Tuning for Reasoning Consistency in Language Models
Large language models (LLMs) with Chain-of-thought (CoT) have recently emerged as a powerful technique for eliciting reasoning to improve various downstream tasks.
Responsibility Perspective Transfer for Italian Femicide News
Different ways of linguistically expressing the same real-world event can lead to different perceptions of what happened.
Pre-Trained Language-Meaning Models for Multilingual Parsing and Generation
Pre-trained language models (PLMs) have achieved great success in NLP and have recently been used for tasks in computational semantics.
Multilingual Multi-Figurative Language Detection
Figures of speech help people express abstract concepts and evoke stronger emotions than literal expressions, thereby making texts more creative and engaging.
Missing Information, Unresponsive Authors, Experimental Flaws: The Impossibility of Assessing the Reproducibility of Previous Human Evaluations in NLP
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/less reproducible.
Multidimensional Evaluation for Text Style Transfer Using ChatGPT
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 well as human judgements.
Multi-Figurative Language Generation
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.
Human Judgement as a Compass to Navigate Automatic Metrics for Formality Transfer
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 metrics, lacking the possibility of always resorting to human judgement.
Multilingual Pre-training with Language and Task Adaptation for Multilingual Text Style Transfer
We exploit the pre-trained seq2seq model mBART for multilingual text style transfer.
Generic resources are what you need: Style transfer tasks without task-specific parallel training data
Style transfer aims to rewrite a source text in a different target style while preserving its content.
Thank you BART! Rewarding Pre-Trained Models Improves Formality Style Transfer
Scarcity of parallel data causes formality style transfer models to have scarce success in preserving content.
On the interaction of automatic evaluation and task framing in headline style transfer
An ongoing debate in the NLG community concerns the best way to evaluate systems, with human evaluation often being considered the most reliable method, compared to corpus-based metrics.
