IJCoL · Italian Journal
of Computational Linguistics

Vol. 7, n. 1-2 · 2021

This special issue on “Computational Dialogue Modelling” discusses recent approaches for modelling pragmatics and common ground in spoken human – human and human – machine interaction. Natural Language Processing (NLP), given the most recent scientific discoveries in the area of intelligent systems and distributed semantics, is now able to build interactive agents whose performance is getting more powerful from year to year. Simple “command-based” models and dialogue state tracking methods are now widely available for very constrained tasks and domains and research in NLP is heading towards the design of more complex scenarios that need to take into account the role of pragmatics in dialogue systems as well as of grounding and commonground.

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Editorial Note
Francesco Cutugno, Hendrik Buschmeier.

Knowledge Modelling for Establishment of Common Ground in Dialogue Systems
Lina Varonina, Stefan Kopp

Pragmatic approach to construct a multimodal corpus: an Italian pilot corpus
Luca Lo Re

How are gestures used by politicians? A multimodal co-gesture analysis
Daniela Trotta, Raffaele Guarasci

Toward Data-Driven Collaborative Dialogue Systems: The JILDA Dataset
Irene Sucameli, Alessandro Lenci, Bernardo Magnini, Manuela Speranza e Maria Simi

Analysis of Empathic Dialogue in Actual Doctor-Patient Calls and Implications for Design of Embodied Conversational Agents
Sana Salman, Deborah Richards

The Role of Moral Values in the Twitter Debate: a Corpus of Conversations
Marco Stranisci, Michele De Leonardis, Cristina Bosco, Viviana Patti

Computational Grounding: An Overview of Common Ground Applications in Conversational Agents
Maria Di Maro

Cutting melted butter? Common Ground inconsistencies management in dialogue systems using graph databases
Maria Di Maro, Antonio Origlia, Francesco Cutugno

Towards a linguistically grounded dialog model for chatbot design
Anna Dall’Acqua, Fabio Tamburini

Improving transfer-learning for Data-to-Text Generation via Preserving High-Frequency Phrases and Fact-Checking
Ethan Joseph, Mei Si, Julian Liaonag