2 research outputs found
Harnessing the Power of Multi-Task Pretraining for Ground-Truth Level Natural Language Explanations
Natural language explanations promise to offer intuitively understandable
explanations of a neural network's decision process in complex vision-language
tasks, as pursued in recent VL-NLE models. While current models offer
impressive performance on task accuracy and explanation plausibility, they
suffer from a range of issues: Some models feature a modular design where the
explanation generation module is poorly integrated with a separate module for
task-answer prediction, employ backbone models trained on limited sets of
tasks, or incorporate ad hoc solutions to increase performance on single
datasets. We propose to evade these limitations by applying recent advances in
large-scale multi-task pretraining of generative Transformer models to the
problem of VL-NLE tasks. Our approach outperforms recent models by a large
margin, with human annotators preferring the generated explanations over the
ground truth in two out of three evaluated datasets. As a novel challenge in
VL-NLE research, we propose the problem of multi-task VL-NLE and show that
jointly training on multiple tasks can increase the explanation quality. We
discuss the ethical implications of high-quality NLE generation and other
issues in recent VL-NLE research.Comment: Minor change
Explain yourself! Effects of Explanations in Human-Robot Interaction
Recent developments in explainable artificial intelligence promise the
potential to transform human-robot interaction: Explanations of robot decisions
could affect user perceptions, justify their reliability, and increase trust.
However, the effects on human perceptions of robots that explain their
decisions have not been studied thoroughly. To analyze the effect of
explainable robots, we conduct a study in which two simulated robots play a
competitive board game. While one robot explains its moves, the other robot
only announces them. Providing explanations for its actions was not sufficient
to change the perceived competence, intelligence, likeability or safety ratings
of the robot. However, the results show that the robot that explains its moves
is perceived as more lively and human-like. This study demonstrates the need
for and potential of explainable human-robot interaction and the wider
assessment of its effects as a novel research direction