no code implementations • EMNLP (SpLU) 2020 • Alberto Testoni, Claudio Greco, Tobias Bianchi, Mauricio Mazuecos, Agata Marcante, Luciana Benotti, Raffaella Bernardi
By analyzing LXMERT errors and its attention mechanisms, we find that our classification helps to gain a better understanding of the skills required to answer different spatial questions.
no code implementations • EMNLP 2021 • Mauricio Mazuecos, Franco M. Luque, Jorge Sánchez, Hernán Maina, Thomas Vadora, Luciana Benotti
Visual Dialog is assumed to require the dialog history to generate correct responses during a dialog.
1 code implementation • ACL (splurobonlp) 2021 • Tianai Dong, Alberto Testoni, Luciana Benotti, Raffaella Bernardi
We call the question that restricts the context: trigger, and we call the spatial question that requires the trigger question to be answered: zoomer.
no code implementations • ReInAct 2021 • Mauricio Mazuecos, Patrick Blackburn, Luciana Benotti
Here we present work in progress in the study of the impact of different answering models in human generated questions in GuessWhat?!.
no code implementations • Findings (NAACL) 2022 • Jorge Sánchez, Mauricio Mazuecos, Hernán Maina, Luciana Benotti
Referring resolution is the task of identifying the referent of a natural language expression, for example “the woman behind the other woman getting a massage”.
1 code implementation • 14 Jul 2022 • Laura Alonso Alemany, Luciana Benotti, Hernán Maina, Lucía González, Mariela Rajngewerc, Lautaro Martínez, Jorge Sánchez, Mauro Schilman, Guido Ivetta, Alexia Halvorsen, Amanda Mata Rojo, Matías Bordone, Beatriz Busaniche
Our methodology is based on the following principles: * focus on the linguistic manifestations of discrimination on word embeddings and language models, not on the mathematical properties of the models * reduce the technical barrier for discrimination experts%, be it social scientists, domain experts or other * characterize through a qualitative exploratory process in addition to a metric-based approach * address mitigation as part of the training process, not as an afterthought
no code implementations • NAACL 2021 • Luciana Benotti, Patrick Blackburn
In this paper, we argue that dialogue clarification mechanisms make explicit the process of interpreting the communicative intents of the speaker's utterances by grounding them in the various modalities in which the dialogue is situated.
no code implementations • EACL 2021 • Luciana Benotti, Patrick Blackburn
Collaborative grounding is a fundamental aspect of human-human dialog which allows people to negotiate meaning.
no code implementations • WS 2020 • Mauricio Mazuecos, Alberto Testoni, Raffaella Bernardi, Luciana Benotti
Regarding our first metric, we find that successful dialogues do not have a higher percentage of effective questions for most models.
no code implementations • WS 2020 • Mauricio Mazuecos, Alberto Testoni, Raffaella Bernardi, Luciana Benotti
Task success is the standard metric used to evaluate these systems.
no code implementations • WS 2018 • Luciana Benotti, Jayadev Bhaskaran, Sigtryggur Kjartansson, David Lang
Knowing how long it would normally take a student to respond to different types of questions could help tutors optimize their own time while answering multiple dialogues concurrently, as well as deciding when to prompt a student again.