most citedRating Prediction in Conversational Task Assistants with Behavioral and Conversational-Flow Features

1 citations · 2 across the 5 of their papers we have counts for

collaborators

5 papers

cs.CL2024

Multi-trait User Simulation with Adaptive Decoding for Conversational Task Assistants

Rafael Ferreira, David Semedo, João Magalhães

Conversational systems must be robust to user interactions that naturally exhibit diverse conversational traits. Capturing and simulating these diverse traits coherently and effici…

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…