6 research outputs found
A Review of Evaluation Techniques for Social Dialogue Systems
In contrast with goal-oriented dialogue, social dialogue has no clear measure
of task success. Consequently, evaluation of these systems is notoriously hard.
In this paper, we review current evaluation methods, focusing on automatic
metrics. We conclude that turn-based metrics often ignore the context and do
not account for the fact that several replies are valid, while end-of-dialogue
rewards are mainly hand-crafted. Both lack grounding in human perceptions.Comment: 2 page
The BURCHAK corpus: a Challenge Data Set for Interactive Learning of Visually Grounded Word Meanings
We motivate and describe a new freely available human-human dialogue dataset
for interactive learning of visually grounded word meanings through ostensive
definition by a tutor to a learner. The data has been collected using a novel,
character-by-character variant of the DiET chat tool (Healey et al., 2003;
Mills and Healey, submitted) with a novel task, where a Learner needs to learn
invented visual attribute words (such as " burchak " for square) from a tutor.
As such, the text-based interactions closely resemble face-to-face conversation
and thus contain many of the linguistic phenomena encountered in natural,
spontaneous dialogue. These include self-and other-correction, mid-sentence
continuations, interruptions, overlaps, fillers, and hedges. We also present a
generic n-gram framework for building user (i.e. tutor) simulations from this
type of incremental data, which is freely available to researchers. We show
that the simulations produce outputs that are similar to the original data
(e.g. 78% turn match similarity). Finally, we train and evaluate a
Reinforcement Learning dialogue control agent for learning visually grounded
word meanings, trained from the BURCHAK corpus. The learned policy shows
comparable performance to a rule-based system built previously.Comment: 10 pages, THE 6TH WORKSHOP ON VISION AND LANGUAGE (VL'17
Learning how to learn: an adaptive dialogue agent for incrementally learning visually grounded word meanings
We present an optimised multi-modal dialogue agent for interactive learning
of visually grounded word meanings from a human tutor, trained on real
human-human tutoring data. Within a life-long interactive learning period, the
agent, trained using Reinforcement Learning (RL), must be able to handle
natural conversations with human users and achieve good learning performance
(accuracy) while minimising human effort in the learning process. We train and
evaluate this system in interaction with a simulated human tutor, which is
built on the BURCHAK corpus -- a Human-Human Dialogue dataset for the visual
learning task. The results show that: 1) The learned policy can coherently
interact with the simulated user to achieve the goal of the task (i.e. learning
visual attributes of objects, e.g. colour and shape); and 2) it finds a better
trade-off between classifier accuracy and tutoring costs than hand-crafted
rule-based policies, including ones with dynamic policies.Comment: 10 pages, RoboNLP Workshop from ACL Conferenc