activity
20182021
most citedCross-Modal Subspace Learning with Scheduled Adaptive Margin Constraints

9 citations · 13 across the 4 of their papers we have counts for

collaborators

15 papers

cs.CL20241 cited

GlórIA -- A Generative and Open Large Language Model for Portuguese

Ricardo Lopes, João Magalhães, David Semedo

Significant strides have been made in natural language tasks, largely attributed to the emergence of powerful large language models (LLMs). These models, pre-trained on extensive a…

cs.CL20241 cited

Plan-Grounded Large Language Models for Dual Goal Conversational Settings

Diogo Glória-Silva, Rafael Ferreira, Diogo Tavares +2

Training Large Language Models (LLMs) to follow user instructions has been shown to supply the LLM with ample capacity to converse fluently while being aligned with humans. Yet, it…

cs.CL20231 cited

Rating Prediction in Conversational Task Assistants with Behavioral and Conversational-Flow Features

Rafael Ferreira, David Semedo, João Magalhães

Predicting the success of Conversational Task Assistants (CTA) can be critical to understand user behavior and act accordingly. In this paper, we propose TB-Rater, a Transformer mo…

cs.CL2023

The Wizard of Curiosities: Enriching Dialogues with Fun Facts

Frederico Vicente, Rafael Ferreira, David Semedo +1

Introducing curiosities in a conversation is a way to teach something new to the person in a pleasant and enjoyable way. Enriching dialogues with contextualized curiosities can imp…

cs.CL2023

Grounded Complex Task Segmentation for Conversational Assistants

Rafael Ferreira, David Semedo, João Magalhães

Following complex instructions in conversational assistants can be quite daunting due to the shorter attention and memory spans when compared to reading the same instructions. Henc…

cs.CL2023

Task Conditioned BERT for Joint Intent Detection and Slot-filling

Diogo Tavares, Pedro Azevedo, David Semedo +2

Dialogue systems need to deal with the unpredictability of user intents to track dialogue state and the heterogeneity of slots to understand user preferences. In this paper we inve…