Measuring Harmful Sentence Completion in Language Models for LGBTQIA+ Individuals

Current language technology is ubiquitous and directly influences individuals' lives worldwide. Given the recent trend in AI on training and constantly releasing new and powerful large language models (LLMs), there is a need to assess their biases …

Pipelines for Social Bias Testing of Large Language Models

The maturity level of language models is now at a stage in which many companies rely on them to solve various tasks. However, while research has shown how biased and harmful these models are, **systematic ways of integrating social bias tests into …

XLM-EMO: Multilingual Emotion Prediction in Social Media Text

We use Large Multilingual Language Models to create a tool for multilingual emotion prediction

Language in a (Search) Box: Grounding Language Learning in Real-World Human-Machine Interaction

We investigate grounded language learning through real-world data, by modelling a teacher-learner dynamics through the natural interactions occurring between users and search engines.

Query2Prod2Vec: Grounded Word Embeddings for eCommerce

We present Query2Prod2Vec, a model that grounds lexical representations for product search in product embeddings: in our model, meaning is a mapping between words and a latent space of products in a digital shop. We leverage shopping sessions to learn the underlying space and use merchandising annotations to build lexical analogies for evaluation: our experiments show that our model is more accurate than known techniques from the NLP and IR literature. Finally, we stress the importance of data efficiency for product search outside of retail giants, and highlight how Query2Prod2Vec fits with practical constraints faced by most practitioners.

Cross-lingual Contextualized Topic Models with Zero-shot Learning

We introduce a novel topic modeling method that can make use of contextulized embeddings (e.g., BERT) to do zero-shot cross-lingual topic modeling.

FEEL-IT: Emotion and Sentiment Classification for the Italian Language

Sentiment analysis is a common task to understand people's reactions online. Still, we often need more nuanced information: is the post negative because the user is angry or because they are sad? An abundance of approaches has been introduced for …