Intimate Partner Violence refers to the abusive behaviours perpetrated on their own partner. Unfortunately this is a social issue that has witnessed an increase over time, particularly after Covid-19. IPV be circumscribed into two broad categories known as Intimate Partner Violence (IPV) and Cyber Intimate Partner Violence (C-IPV). Social Media and technologies can exacerbate these types of behaviors but some “digital footprints”, such as textual conversations, can be exploited by Artificial Intelligence models to detect and, in turn, prevent them. With this aim in mind, this paper describes a scenario in which the Italian Language Model family LLAmAntino can be exploited to explain the presence of toxicity elements in conversations related to teenage relationships and then educate the interlocutor to recognize these elements in the messages received.

The recent introduction of large-scale datasets for the WiC (Word in Context) task enables the creation of more reliable and meaningful contextualized word embeddings.However, most of the approaches to the WiC task use cross-encoders, which prevent the possibility of deriving comparable word embeddings.In this work, we introduce XL-LEXEME, a Lexical Semantic Change Detection model.XL-LEXEME extends SBERT, highlighting the target word in the sentence. We evaluate XL-LEXEME on the multilingual benchmarks for SemEval-2020 Task 1 - Lexical Semantic Change (LSC) Detection and the RuShiftEval shared task involving five languages: English, German, Swedish, Latin, and Russian.XL-LEXEME outperforms the state-of-the-art in English, German and Swedish with statistically significant differences from the baseline results and obtains state-of-the-art performance in the RuShiftEval shared task.

In this paper, we introduce the results of our submitted system to the FinTOC 2022 task. We address the task using a two-stage process: first, we detect titles using Document Image Analysis, then we train a supervised model for the hierarchical level prediction. We perform Document Image Analysis using a pre-trained Faster R-CNN on the PublyaNet dataset. We fine-tuned the model on the FinTOC 2022 training set. We extract orthographic and layout features from detected titles and use them to train a Random Forest model to predict the title level. The proposed system ranked #1 on both Title Detection and the Table of Content extraction tasks for Spanish. The system ranked #3 on both the two subtasks for English and French.