1 code implementation • WASSA (ACL) 2022 • Seyed Mahed Mousavi, Gabriel Roccabruna, Aniruddha Tammewar, Steve Azzolin, Giuseppe Riccardi
Deep Neural Networks (DNN) models have achieved acceptable performance in sentiment prediction of written text.
no code implementations • NAACL (NLPMC) 2021 • Seyed Mahed Mousavi, Alessandra Cervone, Morena Danieli, Giuseppe Riccardi
The acquisition of a dialogue corpus is a key step in the process of training a dialogue model.
no code implementations • LREC 2022 • Aniruddha Tammewar, Franziska Braun, Gabriel Roccabruna, Sebastian Bayerl, Korbinian Riedhammer, Giuseppe Riccardi
In this work, we annotate a corpus of spoken personal narratives, with the emotion valence using discrete values.
1 code implementation • LREC 2022 • Gabriel Roccabruna, Steve Azzolin, Giuseppe Riccardi
Sentiment analysis is one of the most widely studied tasks in natural language processing.
1 code implementation • 10 Apr 2024 • Seyed Mahed Mousavi, Simone Alghisi, Giuseppe Riccardi
We study the appropriateness of Large Language Models (LLMs) as knowledge repositories.
no code implementations • 4 Jan 2024 • Seyed Mahed Mousavi, Gabriel Roccabruna, Simone Alghisi, Massimo Rizzoli, Mirco Ravanelli, Giuseppe Riccardi
Large Pre-Trained Language Models have demonstrated state-of-the-art performance in different downstream tasks, including dialogue state tracking and end-to-end response generation.
1 code implementation • 4 Aug 2023 • Michele Yin, Gabriel Roccabruna, Abhinav Azad, Giuseppe Riccardi
In this work, we present an open-source architecture with the goal of simplifying the development of conversational agents operating in virtual environments.
no code implementations • 27 May 2023 • Gabriel Roccabruna, Seyed Mahed Mousavi, Giuseppe Riccardi
We further observed that the discriminative model achieves the best trade-off of valence and EC prediction tasks in the joint prediction setting.
no code implementations • 25 May 2023 • Seyed Mahed Mousavi, Simone Caldarella, Giuseppe Riccardi
Dialogue systems designed for LDs should uniquely interact with the users over multiple sessions and long periods of time (e. g. weeks), and engage them in personal dialogues to elaborate on their feelings, thoughts, and real-life events.
1 code implementation • 15 Feb 2023 • Seyed Mahed Mousavi, Shohei Tanaka, Gabriel Roccabruna, Koichiro Yoshino, Satoshi Nakamura, Giuseppe Riccardi
We publish the annotated dataset, annotation materials, and machine learning baseline models for the task of new event extraction for narrative understanding.
no code implementations • 17 Jun 2022 • Sebastian P. Bayerl, Gabriel Roccabruna, Shammur Absar Chowdhury, Tommaso Ciulli, Morena Danieli, Korbinian Riedhammer, Giuseppe Riccardi
To the best of our knowledge, this is the first and a novel study to exploit speech and language for characterising working alliance.
no code implementations • 13 Dec 2021 • Sebastian P. Bayerl, Aniruddha Tammewar, Korbinian Riedhammer, Giuseppe Riccardi
However, in this work, we focus on Emotion Carriers (EC) defined as the segments (speech or text) that best explain the emotional state of the narrator ("loss of father", "made me choose").
1 code implementation • 25 Nov 2021 • Juan Manuel Mayor-Torres, Sara Medina-DeVilliers, Tessa Clarkson, Matthew D. Lerner, Giuseppe Riccardi
This study is the first to consolidate a more transparent feature-relevance calculation for a successful EEG-based facial emotion recognition using a within-subject-trained CNN in typically-developed and ASD individuals.
1 code implementation • 18 Jul 2021 • Juan Manuel Mayor-Torres, Mirco Ravanelli, Sara E. Medina-DeVilliers, Matthew D. Lerner, Giuseppe Riccardi
This result is consistent with recent neuroscience studies on emotion recognition, which found an association between these band suppressions and the behavioral deficits observed in individuals with ASD.
no code implementations • 17 Aug 2020 • Aniruddha Tammewar, Alessandra Cervone, Giuseppe Riccardi
In this work, we propose a novel task for Narrative Understanding: Emotion Carrier Recognition (ECR).
2 code implementations • SIGDIAL (ACL) 2020 • Alessandra Cervone, Giuseppe Riccardi
In this work, we investigate the human perception of coherence in open-domain dialogues.
no code implementations • LREC 2020 • Aniruddha Tammewar, Alessandra Cervone, Eva-Maria Messner, Giuseppe Riccardi
We are interested in the problem of understanding personal narratives (PN) - spoken or written - recollections of facts, events, and thoughts.
no code implementations • WS 2019 • Giuliano Tortoreto, Evgeny A. Stepanov, Alessandra Cervone, Mateusz Dubiel, Giuseppe Riccardi
Possible applications of the method include provision of guidelines that highlight potential implications of using such platforms on users' mental health, and/or support in the analysis of their impact on specific individuals.
no code implementations • 12 Aug 2019 • Federico Marinelli, Alessandra Cervone, Giuliano Tortoreto, Evgeny A. Stepanov, Giuseppe Di Fabbrizio, Giuseppe Riccardi
Natural Language Understanding (NLU) models are typically trained in a supervised learning framework.
2 code implementations • 28 May 2019 • Andrei C. Coman, Koichiro Yoshino, Yukitoshi Murase, Satoshi Nakamura, Giuseppe Riccardi
To identify the point of maximal understanding in an ongoing utterance, we a) implement an incremental Dialog State Tracker which is updated on a token basis (iDST) b) re-label the Dialog State Tracking Challenge 2 (DSTC2) dataset and c) adapt it to the incremental turn-taking experimental scenario.
no code implementations • 9 May 2019 • Aniruddha Tammewar, Alessandra Cervone, Eva-Maria Messner, Giuseppe Riccardi
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. early diagnosis of mental diseases, supervision of disease course, etc.).
1 code implementation • 27 Jul 2018 • Jacopo Gobbi, Evgeny Stepanov, Giuseppe Riccardi
The third contribution is the release of a repository of the algorithms, datasets and recipes for NLU evaluation.
Natural Language Understanding Vocal Bursts Valence Prediction
1 code implementation • 21 Jun 2018 • Alessandra Cervone, Evgeny Stepanov, Giuseppe Riccardi
Nevertheless, both the original grid and its extensions do not model intents, a crucial aspect that has been studied widely in the literature in connection to dialogue structure.
1 code implementation • COLING 2018 • Stefano Mezza, Alessandra Cervone, Giuliano Tortoreto, Evgeny A. Stepanov, Giuseppe Riccardi
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 system.
no code implementations • 10 Nov 2017 • Evgeny Stepanov, Stephane Lathuiliere, Shammur Absar Chowdhury, Arindam Ghosh, Radu-Laurentiu Vieriu, Nicu Sebe, Giuseppe Riccardi
In this AVEC challenge we explore different modalities (speech, language and visual features extracted from face) to design and develop automatic methods for the detection of depression.
no code implementations • WS 2017 • Karan Singla, Evgeny Stepanov, Ali Orkan Bayer, Giuseppe Carenini, Giuseppe Riccardi
Summarization of spoken conversations is a challenging task, since it requires deep understanding of dialogs.
no code implementations • WS 2017 • Shammur Absar Chowdhury, Evgeny Stepanov, Morena Danieli, Giuseppe Riccardi
It is also observed that sometimes long silences can be used deliberately to get a forced response from another speaker thus making silence a multi-functional and an important catalyst towards information flow.
no code implementations • 13 May 2017 • Firoj Alam, Morena Danieli, Giuseppe Riccardi
The automatic classification system was evaluated on call center conversations where it showed significantly better performance than the baseline.
no code implementations • WS 2016 • Firoj Alam, Fabio Celli, Evgeny A. Stepanov, Arindam Ghosh, Giuseppe Riccardi
In this paper, we address the issue of automatic prediction of readers{'} mood from newspaper articles and comments.
no code implementations • WS 2016 • Fabio Celli, Evgeny Stepanov, Massimo Poesio, Giuseppe Riccardi
On June 23rd 2016, UK held the referendum which ratified the exit from the EU.
no code implementations • COLING 2016 • Firoj Alam, Shammur Absar Chowdhury, Morena Danieli, Giuseppe Riccardi
In this paper, we aim to investigate the coordination of interlocutors behavior in different emotional segments.
no code implementations • LREC 2016 • Morena Danieli, Balamurali A.R, Evgeny Stepanov, Benoit Favre, Frederic Bechet, Giuseppe Riccardi
Annotating and predicting behavioural aspects in conversations is becoming critical in the conversational analytics industry.
no code implementations • LREC 2016 • Fabio Celli, Giuseppe Riccardi, Firoj Alam
In this paper, we present a corpus of news blog conversations in Italian annotated with gold standard agreement/disagreement relations at message and sentence levels.
no code implementations • LREC 2016 • Shammur Absar Chowdhury, Evgeny Stepanov, Giuseppe Riccardi
In this paper we test the utility of the ISO standard through comparative evaluation of the corpus-specific legacy and the semi-automatically transferred DiAML DA annotations on supervised dialogue act classification task.
no code implementations • LREC 2014 • Evgeny Stepanov, Giuseppe Riccardi, Ali Orkan Bayer
We discuss the challenges of the manual creation of multilingual corpora, as well as present the algorithms for the creation of multilingual SLU via Statistical Machine Translation (SMT).
no code implementations • LREC 2012 • Sucheta Ghosh, Richard Johansson, Giuseppe Riccardi, Sara Tonelli
We describe two constraint-based methods that can be used to improve the recall of a shallow discourse parser based on conditional random field chunking.