Since the advent of Transformer-based, pretrained language models (LM) such as BERT, Natural Language Understanding (NLU) components in the form of Dialogue Act Recognition (DAR) and Slot Recognition (SR) for dialogue systems have become both more accurate and easier to create for specific application domains. Unsurprisingly however, much of this progress has been limited to the English language, due to the existence of very large datasets in both dialogue and written form, while only few corpora are available for lower resourced languages like Italian. In this paper, we present JILDA 2.0, an enhanced version of a Italian task-oriented dialogue dataset, using it to realise a Italian NLU baseline by evaluating three of the most recent pretrained LMs: Italian BERT, Multilingual BERT, and AlBERTo for the DAR and SR tasks. Thus, this paper not only presents an updated version of a dataset characterised by complex dialogues, but it also highlights the challenges that still remain in creating effective NLU components for lower resourced languages, constituting a first step in improving NLU for Italian dialogue.

Dialogue Systems are becoming ubiquitous in various forms and shapes - virtual assistants(Siri, Alexa, etc.), chat-bots, customer sup-port, chit-chat systems just to name a few. The advances in language models and their publication have democratised advanced NLP.However, data remains a crucial bottleneck. Our contribution to this essential pillar isMATILDA, to the best of our knowledge the first multi-annotator, multi-language dialogue annotation tool. MATILDA allows the creation of corpora, the management of users, the annotation of dialogues, the quick adaptation of the user interface to any language and the resolution of inter-annotator disagreement. We evaluate the tool on ease of use, annotation speed and interannotation resolution for both experts and novices and conclude that this tool not only supports the full pipeline for dialogue annotation, but also allows non-technical people to easily use it. We are completely open-sourcing the tool at https://github.com/wluper/matilda and provide a tutorial video1.

Is it possible to use images to model verb semantic similarities? Starting from this core question, we developed two textual distributional semantic models and a visual one. We found particularly interesting and challenging to investigate this Part of Speech since verbs are not often analysed in researches focused on multimodal distributional semantics. After the creation of the visual and textual distributional space, the three models were evaluated in relation to SimLex-999, a gold standard resource. Through this evaluation, we demonstrate that, using visual distributional models, it is possible to extract meaningful information and to effectively capture the semantic similarity between verbs.