Search Results for author:
Found 7 papers, 3 papers with code
“It seemed like an annoying woman”: On the Perception and Ethical Considerations of Affective Language in Text-Based Conversational Agents
Based on statistical and qualitative analysis of the responses, we found language style played an important role in how human-like participants perceived a dialog agent as well as how likable.
A Zero-Shot approach to the Conversational Tree Search Task
In sensitive domains, such as legal or medial domains, the correctness of information given to users is critical.
Investigating the effect of Mental Models in User Interaction with an Adaptive Dialog Agent
We demonstrate that users have a variety of conflicting mental models about such systems, the validity of which directly impacts the success of their interactions and perceived usability of system.
Towards a Zero-Data, Controllable, Adaptive Dialog System
Conversational Tree Search (V\"ath et al., 2023) is a recent approach to controllable dialog systems, where domain experts shape the behavior of a Reinforcement Learning agent through a dialog tree.
Conversational Tree Search: A New Hybrid Dialog Task
Conversational interfaces provide a flexible and easy way for users to seek information that may otherwise be difficult or inconvenient to obtain.
Beyond Accuracy: A Consolidated Tool for Visual Question Answering Benchmarking
On the way towards general Visual Question Answering (VQA) systems that are able to answer arbitrary questions, the need arises for evaluation beyond single-metric leaderboards for specific datasets.
ADVISER: A Toolkit for Developing Multi-modal, Multi-domain and Socially-engaged Conversational Agents
We present ADVISER - an open-source, multi-domain dialog system toolkit that enables the development of multi-modal (incorporating speech, text and vision), socially-engaged (e. g. emotion recognition, engagement level prediction and backchanneling) conversational agents.
