TailNLG: A Multilingual Benchmark Addressing Verbalization of Long-Tail Entities

The automatic verbalization of structured knowledge is a key task for making knowledge graphs accessible to non-expert users and supporting retrieval-augmented generation systems. Although recent advances in Data-to-Text generation have improved multilingual coverage, little attention has been paid to potential biases in the verbalization of rare entities, frequently known as long-tail entities. In this work, we present the first systematic study of long-tail entities in Data-to-Text generation. We introduce TailNLG, a new multilingual benchmark in English, Italian, and Spanish, built from Wikidata and covering entities with varying levels of popularity. We evaluate three different families of large language models in zero-shot settings and compare their performance on rare versus common entities, as well as against the established WebNLG benchmark. Our results reveal a consistent bias against long-tail entities: embedding-based scores are lower, and model uncertainty is higher for rare entities. We further show that the impact of long-tail entities varies across models and languages, and that existing evaluation metrics do not consistently capture these differences, highlighting the need for more reliable evaluation frameworks.

Preliminary results of a therapeutic lab for promoting autonomies in autistic children

This extended abtract describes the preliminary qualitative results coming from a therapeutic laboratory focused on the use of the Pepper robot to promote autonomies and functional acquisitions in highly functioning (Asperger) children with autism. The field lab, ideated and led by a multidisciplinary team, involved 4 children, aged 11-13, who attended the laboratory sessions once a week for four months.

Autistic Children's Mental Model of an Humanoid Robot

This position paper introduces the results of an initial card sorting experiment based on the reactions and questions of a group of children with autism working with a humanoid robot in a therapeutic laboratory on autonomy.

UX Personas for defining robot's character and personality

This paper describes the use of UX Personas for defining a stable character for the Pepper robot. This approach generated from the first analysis of the social interactions that occurred between Pepper and a group of children with autism working with the robot in a therapeutic laboratory on autonomy.