450 research outputs found
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
Neural Response Ranking for Social Conversation: A Data-Efficient Approach
The overall objective of 'social' dialogue systems is to support engaging,
entertaining, and lengthy conversations on a wide variety of topics, including
social chit-chat. Apart from raw dialogue data, user-provided ratings are the
most common signal used to train such systems to produce engaging responses. In
this paper we show that social dialogue systems can be trained effectively from
raw unannotated data. Using a dataset of real conversations collected in the
2017 Alexa Prize challenge, we developed a neural ranker for selecting 'good'
system responses to user utterances, i.e. responses which are likely to lead to
long and engaging conversations. We show that (1) our neural ranker
consistently outperforms several strong baselines when trained to optimise for
user ratings; (2) when trained on larger amounts of data and only using
conversation length as the objective, the ranker performs better than the one
trained using ratings -- ultimately reaching a Precision@1 of 0.87. This
advance will make data collection for social conversational agents simpler and
less expensive in the future.Comment: 2018 EMNLP Workshop SCAI: The 2nd International Workshop on
Search-Oriented Conversational AI. Brussels, Belgium, October 31, 201
Natural Language Generation enhances human decision-making with uncertain information
Decision-making is often dependent on uncertain data, e.g. data associated
with confidence scores or probabilities. We present a comparison of different
information presentations for uncertain data and, for the first time, measure
their effects on human decision-making. We show that the use of Natural
Language Generation (NLG) improves decision-making under uncertainty, compared
to state-of-the-art graphical-based representation methods. In a task-based
study with 442 adults, we found that presentations using NLG lead to 24% better
decision-making on average than the graphical presentations, and to 44% better
decision-making when NLG is combined with graphics. We also show that women
achieve significantly better results when presented with NLG output (an 87%
increase on average compared to graphical presentations).Comment: 54th annual meeting of the Association for Computational Linguistics
(ACL), Berlin 201
Challenging Neural Dialogue Models with Natural Data: Memory Networks Fail on Incremental Phenomena
Natural, spontaneous dialogue proceeds incrementally on a word-by-word basis;
and it contains many sorts of disfluency such as mid-utterance/sentence
hesitations, interruptions, and self-corrections. But training data for machine
learning approaches to dialogue processing is often either cleaned-up or wholly
synthetic in order to avoid such phenomena. The question then arises of how
well systems trained on such clean data generalise to real spontaneous
dialogue, or indeed whether they are trainable at all on naturally occurring
dialogue data. To answer this question, we created a new corpus called bAbI+ by
systematically adding natural spontaneous incremental dialogue phenomena such
as restarts and self-corrections to the Facebook AI Research's bAbI dialogues
dataset. We then explore the performance of a state-of-the-art retrieval model,
MemN2N, on this more natural dataset. Results show that the semantic accuracy
of the MemN2N model drops drastically; and that although it is in principle
able to learn to process the constructions in bAbI+, it needs an impractical
amount of training data to do so. Finally, we go on to show that an
incremental, semantic parser -- DyLan -- shows 100% semantic accuracy on both
bAbI and bAbI+, highlighting the generalisation properties of linguistically
informed dialogue models.Comment: 9 pages, 3 figures, 2 tables. Accepted as a full paper for SemDial
201
Crowd-sourcing NLG Data: Pictures Elicit Better Data
Recent advances in corpus-based Natural Language Generation (NLG) hold the
promise of being easily portable across domains, but require costly training
data, consisting of meaning representations (MRs) paired with Natural Language
(NL) utterances. In this work, we propose a novel framework for crowdsourcing
high quality NLG training data, using automatic quality control measures and
evaluating different MRs with which to elicit data. We show that pictorial MRs
result in better NL data being collected than logic-based MRs: utterances
elicited by pictorial MRs are judged as significantly more natural, more
informative, and better phrased, with a significant increase in average quality
ratings (around 0.5 points on a 6-point scale), compared to using the logical
MRs. As the MR becomes more complex, the benefits of pictorial stimuli
increase. The collected data will be released as part of this submission.Comment: The 9th International Natural Language Generation conference INLG,
2016. 10 pages, 2 figures, 3 table
Training an adaptive dialogue policy for interactive learning of visually grounded word meanings
We present a multi-modal dialogue system for interactive learning of
perceptually grounded word meanings from a human tutor. The system integrates
an incremental, semantic parsing/generation framework - Dynamic Syntax and Type
Theory with Records (DS-TTR) - with a set of visual classifiers that are
learned throughout the interaction and which ground the meaning representations
that it produces. We use this system in interaction with a simulated human
tutor to study the effects of different dialogue policies and capabilities on
the accuracy of learned meanings, learning rates, and efforts/costs to the
tutor. We show that the overall performance of the learning agent is affected
by (1) who takes initiative in the dialogues; (2) the ability to express/use
their confidence level about visual attributes; and (3) the ability to process
elliptical and incrementally constructed dialogue turns. Ultimately, we train
an adaptive dialogue policy which optimises the trade-off between classifier
accuracy and tutoring costs.Comment: 11 pages, SIGDIAL 2016 Conferenc
Sympathy Begins with a Smile, Intelligence Begins with a Word: Use of Multimodal Features in Spoken Human-Robot Interaction
Recognition of social signals, from human facial expressions or prosody of
speech, is a popular research topic in human-robot interaction studies. There
is also a long line of research in the spoken dialogue community that
investigates user satisfaction in relation to dialogue characteristics.
However, very little research relates a combination of multimodal social
signals and language features detected during spoken face-to-face human-robot
interaction to the resulting user perception of a robot. In this paper we show
how different emotional facial expressions of human users, in combination with
prosodic characteristics of human speech and features of human-robot dialogue,
correlate with users' impressions of the robot after a conversation. We find
that happiness in the user's recognised facial expression strongly correlates
with likeability of a robot, while dialogue-related features (such as number of
human turns or number of sentences per robot utterance) correlate with
perceiving a robot as intelligent. In addition, we show that facial expression,
emotional features, and prosody are better predictors of human ratings related
to perceived robot likeability and anthropomorphism, while linguistic and
non-linguistic features more often predict perceived robot intelligence and
interpretability. As such, these characteristics may in future be used as an
online reward signal for in-situ Reinforcement Learning based adaptive
human-robot dialogue systems.Comment: Robo-NLP workshop at ACL 2017. 9 pages, 5 figures, 6 table
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