Despite the remarkable success recently obtained by Large Language Models, a significant gap in performance still exists when dealing with low-resource languages which are often poorly supported by off-the-shelf models. In this work we focus on Fassa Ladin, a Rhaeto-Romance linguistic variety spoken by less than ten thousand people in the Dolomitic regions, and set to build the first bidirectional Machine Translation system supporting Italian, English, and Fassa Ladin. To this end, we collected a small though representative corpus compounding 1135 parallel sentences in these three languages, and spanning five domains. We evaluated several models including the open (Meta AI’s No Language Left Behind, NLLB-200) and commercial (OpenAI’s gpt-4o) state-of-the-art, and indeed found that both obtain unsatisfactory performance. We therefore proceeded to finetune the NLLB-200 model on the data collected, using different approaches. We report a comparative analysis of the results obtained, showing that 1) jointly training for multilingual translation (Ladin-Italian and Ladin-English) significantly improves the performance, and 2) knowledge-transfer is highly effective (e.g., leveraging similarities between Ladin and Friulian), highlighting the importance of targeted data collection and model adaptation in the context of low-resource/endangered languages for which little textual data is available.

Current text classification approaches usually focus on the content to be classified. Contextual aspects (both linguistic and extra-linguistic) are usually neglected, even in tasks based on online discussions. Still in many cases the multi-party and multi-turn nature of the context from which these elements are selected can be fruitfully exploited. In this work, we propose a series of experiments on a large dataset for stance detection in English, in which we evaluate the contribution of different types of contextual information, i.e. linguistic, structural and temporal, by feeding them as natural language input into a transformer-based model. We also experiment with different amounts of training data and analyse the topology of local discussion networks in a privacy-compliant way. Results show that structural information can be highly beneficial to text classification but only under certain circumstances (e.g. depending on the amount of training data and on discussion chain complexity). Indeed, we show that contextual information on smaller datasets from other classification tasks does not yield significant improvements. Our framework, based on local discussion networks, allows the integration of structural information while minimising user profiling, thus preserving their privacy.

Assessing the performance of systems to classify Multi-Party Conversations (MPC) is challenging due to the interconnection between linguistic and structural characteristics of conversations. Conventional evaluation methods often overlook variances in model behavior across different levels of structural complexity on interaction graphs. In this work, we propose a methodological pipeline to investigate model performance across specific structural attributes of conversations. As a proof of concept we focus on Response Selection and Addressee Recognition tasks, to diagnose model weaknesses. To this end, we extract representative diagnostic subdatasets with a fixed number of users and a good structural variety from a large and open corpus of online MPCs. We further frame our work in terms of data minimization, avoiding the use of original usernames to preserve privacy, and propose alternatives to using original text messages. Results show that response selection relies more on the textual content of conversations, while addressee recognition requires capturing their structural dimension. Using an LLM in a zero-shot setting, we further highlight how sensitivity to prompt variations is task-dependent.