41 citations · 75 across the 15 of their papers we have counts for
19 papers
Let's Give a Voice to Conversational Agents in Virtual Reality
Michele Yin, Gabriel Roccabruna, Abhinav Azad +1
The dialogue experience with conversational agents can be greatly enhanced with multimodal and immersive interactions in virtual reality. In this work, we present an open-source ar…
Response Generation in Longitudinal Dialogues: Which Knowledge Representation Helps?
Seyed Mahed Mousavi, Simone Caldarella, Giuseppe Riccardi
Longitudinal Dialogues (LD) are the most challenging type of conversation for human-machine dialogue systems. LDs include the recollections of events, personal thoughts, and emotio…
Understanding Emotion Valence is a Joint Deep Learning Task
Gabriel Roccabruna, Seyed Mahed Mousavi, Giuseppe Riccardi
The valence analysis of speakers' utterances or written posts helps to understand the activation and variations of the emotional state throughout the conversation. More recently, t…
Whats New? Identifying the Unfolding of New Events in Narratives
Seyed Mahed Mousavi, Shohei Tanaka, Gabriel Roccabruna +3
Narratives include a rich source of events unfolding over time and context. Automatic understanding of these events provides a summarised comprehension of the narrative for further…
Detecting Emotion Carriers by Combining Acoustic and Lexical Representations
Sebastian P. Bayerl, Aniruddha Tammewar, Korbinian Riedhammer +1
Personal narratives (PN) - spoken or written - are recollections of facts, people, events, and thoughts from one's own experience. Emotion recognition and sentiment analysis tasks…
Evaluation of Interpretability for Deep Learning algorithms in EEG Emotion Recognition: A case study in Autism
Juan Manuel Mayor-Torres, Sara Medina-DeVilliers, Tessa Clarkson +2
Current models on Explainable Artificial Intelligence (XAI) have shown an evident and quantified lack of reliability for measuring feature-relevance when statistically entangled fe…
Interpretable SincNet-based Deep Learning for Emotion Recognition from EEG brain activity
Juan Manuel Mayor-Torres, Mirco Ravanelli, Sara E. Medina-DeVilliers +2
Machine learning methods, such as deep learning, show promising results in the medical domain. However, the lack of interpretability of these algorithms may hinder their applicabil…
Emotion Carrier Recognition from Personal Narratives
Aniruddha Tammewar, Alessandra Cervone, Giuseppe Riccardi
Personal Narratives (PN) - recollections of facts, events, and thoughts from one's own experience - are often used in everyday conversations. So far, PNs have mainly been explored…
Is this Dialogue Coherent? Learning from Dialogue Acts and Entities
Alessandra Cervone, Giuseppe Riccardi
In this work, we investigate the human perception of coherence in open-domain dialogues. In particular, we address the problem of annotating and modeling the coherence of next-turn…
Annotation of Emotion Carriers in Personal Narratives
Aniruddha Tammewar, Alessandra Cervone, Eva-Maria Messner +1
We are interested in the problem of understanding personal narratives (PN) - spoken or written - recollections of facts, events, and thoughts. In PN, emotion carriers are the speec…
Affective Behaviour Analysis of On-line User Interactions: Are On-line Support Groups more Therapeutic than Twitter?
Giuliano Tortoreto, Evgeny A. Stepanov, Alessandra Cervone +2
The increase in the prevalence of mental health problems has coincided with a growing popularity of health related social networking sites. Regardless of their therapeutic potentia…
Active Annotation: bootstrapping annotation lexicon and guidelines for supervised NLU learning
Federico Marinelli, Alessandra Cervone, Giuliano Tortoreto +3
Natural Language Understanding (NLU) models are typically trained in a supervised learning framework. In the case of intent classification, the predicted labels are predefined and…
An Incremental Turn-Taking Model For Task-Oriented Dialog Systems
Andrei C. Coman, Koichiro Yoshino, Yukitoshi Murase +2
In a human-machine dialog scenario, deciding the appropriate time for the machine to take the turn is an open research problem. In contrast, humans engaged in conversations are abl…
Modeling user context for valence prediction from narratives
Aniruddha Tammewar, Alessandra Cervone, Eva-Maria Messner +1
Automated prediction of valence, one key feature of a person's emotional state, from individuals' personal narratives may provide crucial information for mental healthcare (e.g. ea…
Concept Tagging for Natural Language Understanding: Two Decadelong Algorithm Development
Jacopo Gobbi, Evgeny Stepanov, Giuseppe Riccardi
Concept tagging is a type of structured learning needed for natural language understanding (NLU) systems. In this task, meaning labels from a domain ontology are assigned to word s…
Coherence Models for Dialogue
Alessandra Cervone, Evgeny Stepanov, Giuseppe Riccardi
Coherence across multiple turns is a major challenge for state-of-the-art dialogue models. Arguably the most successful approach to automatically learning text coherence is the ent…
ISO-Standard Domain-Independent Dialogue Act Tagging for Conversational Agents
Stefano Mezza, Alessandra Cervone, Giuliano Tortoreto +2
Dialogue Act (DA) tagging is crucial for spoken language understanding systems, as it provides a general representation of speakers' intents, not bound to a particular dialogue sys…
Depression Severity Estimation from Multiple Modalities
Evgeny Stepanov, Stephane Lathuiliere, Shammur Absar Chowdhury +4
Depression is a major debilitating disorder which can affect people from all ages. With a continuous increase in the number of annual cases of depression, there is a need to develo…
Annotating and Modeling Empathy in Spoken Conversations
Firoj Alam, Morena Danieli, Giuseppe Riccardi
Empathy, as defined in behavioral sciences, expresses the ability of human beings to recognize, understand and react to emotions, attitudes and beliefs of others. The lack of an op…