216 citations · 514 across the 35 of their papers we have counts for
38 papers
Births are difficult to predict even with rich survey and full-population register data
Elizaveta Sivak, Emily M. Cantrell, Thomas Emery +109
Major life events have proven difficult to predict. Does this reflect limits of theory, data, and algorithms, or the large role of chance? We examine one outcome - having a child w…
Combining the Strengths of Dutch Survey and Register Data in a Data Challenge to Predict Fertility (PreFer)
Elizaveta Sivak, Paulina Pankowska, Adrienne Mendrik +8
The social sciences have produced an impressive body of research on determinants of fertility outcomes, or whether and when people have children. However, the strength of these det…
Quantifying the Plausibility of Context Reliance in Neural Machine Translation
Gabriele Sarti, Grzegorz Chrupała, Malvina Nissim +1
Establishing whether language models can use contextual information in a human-plausible way is important to ensure their trustworthiness in real-world settings. However, the quest…
Let the Models Respond: Interpreting Language Model Detoxification Through the Lens of Prompt Dependence
Daniel Scalena, Gabriele Sarti, Malvina Nissim +1
Due to language models' propensity to generate toxic or hateful responses, several techniques were developed to align model generations with users' preferences. Despite the effecti…
Responsibility Perspective Transfer for Italian Femicide News
Gosse Minnema, Huiyuan Lai, Benedetta Muscato +1
Different ways of linguistically expressing the same real-world event can lead to different perceptions of what happened. Previous work has shown that different descriptions of gen…
Pre-Trained Language-Meaning Models for Multilingual Parsing and Generation
Chunliu Wang, Huiyuan Lai, Malvina Nissim +1
Pre-trained language models (PLMs) have achieved great success in NLP and have recently been used for tasks in computational semantics. However, these tasks do not fully benefit fr…
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…
DUMB: A Benchmark for Smart Evaluation of Dutch Models
Wietse de Vries, Martijn Wieling, Malvina Nissim
We introduce the Dutch Model Benchmark: DUMB. The benchmark includes a diverse set of datasets for low-, medium- and high-resource tasks. The total set of nine tasks includes four…
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…
Inseq: An Interpretability Toolkit for Sequence Generation Models
Gabriele Sarti, Nils Feldhus, Ludwig Sickert +3
Past work in natural language processing interpretability focused mainly on popular classification tasks while largely overlooking generation settings, partly due to a lack of dedi…
Dead or Murdered? Predicting Responsibility Perception in Femicide News Reports
Gosse Minnema, Sara Gemelli, Chiara Zanchi +2
Different linguistic expressions can conceptualize the same event from different viewpoints by emphasizing certain participants over others. Here, we investigate a case where this…
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…
SOCIOFILLMORE: A Tool for Discovering Perspectives
Gosse Minnema, Sara Gemelli, Chiara Zanchi +2
SOCIOFILLMORE is a multilingual tool which helps to bring to the fore the focus or the perspective that a text expresses in depicting an event. Our tool, whose rationale we also su…
IT5: Text-to-text Pretraining for Italian Language Understanding and Generation
Gabriele Sarti, Malvina Nissim
We introduce IT5, the first family of encoder-decoder transformer models pretrained specifically on Italian. We document and perform a thorough cleaning procedure for a large Itali…
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…
Adapting Monolingual Models: Data can be Scarce when Language Similarity is High
Wietse de Vries, Martijn Bartelds, Malvina Nissim +1
For many (minority) languages, the resources needed to train large models are not available. We investigate the performance of zero-shot transfer learning with as little data as po…
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…
On the interaction of automatic evaluation and task framing in headline style transfer
Lorenzo De Mattei, Michele Cafagna, Huiyuan Lai +3
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 me…
As Good as New. How to Successfully Recycle English GPT-2 to Make Models for Other Languages
Wietse de Vries, Malvina Nissim
Large generative language models have been very successful for English, but other languages lag behind, in part due to data and computational limitations. We propose a method that…
Datasets and Models for Authorship Attribution on Italian Personal Writings
Gaetana Ruggiero, Albert Gatt, Malvina Nissim
Existing research on Authorship Attribution (AA) focuses on texts for which a lot of data is available (e.g novels), mainly in English. We approach AA via Authorship Verification o…
Matching Theory and Data with Personal-ITY: What a Corpus of Italian YouTube Comments Reveals About Personality
Elisa Bassignana, Malvina Nissim, Viviana Patti
As a contribution to personality detection in languages other than English, we rely on distant supervision to create Personal-ITY, a novel corpus of YouTube comments in Italian, wh…