177 research outputs found
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
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
Collaborative Inference of Coexisting Information Diffusions
Recently, \textit{diffusion history inference} has become an emerging
research topic due to its great benefits for various applications, whose
purpose is to reconstruct the missing histories of information diffusion traces
according to incomplete observations. The existing methods, however, often
focus only on single information diffusion trace, while in a real-world social
network, there often coexist multiple information diffusions over the same
network. In this paper, we propose a novel approach called Collaborative
Inference Model (CIM) for the problem of the inference of coexisting
information diffusions. By exploiting the synergism between the coexisting
information diffusions, CIM holistically models multiple information diffusions
as a sparse 4th-order tensor called Coexisting Diffusions Tensor (CDT) without
any prior assumption of diffusion models, and collaboratively infers the
histories of the coexisting information diffusions via a low-rank approximation
of CDT with a fusion of heterogeneous constraints generated from additional
data sources. To improve the efficiency, we further propose an optimal
algorithm called Time Window based Parallel Decomposition Algorithm (TWPDA),
which can speed up the inference without compromise on the accuracy by
utilizing the temporal locality of information diffusions. The extensive
experiments conducted on real world datasets and synthetic datasets verify the
effectiveness and efficiency of CIM and TWPDA
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
Provable Unrestricted Adversarial Training without Compromise with Generalizability
Adversarial training (AT) is widely considered as the most promising strategy
to defend against adversarial attacks and has drawn increasing interest from
researchers. However, the existing AT methods still suffer from two challenges.
First, they are unable to handle unrestricted adversarial examples (UAEs),
which are built from scratch, as opposed to restricted adversarial examples
(RAEs), which are created by adding perturbations bound by an norm to
observed examples. Second, the existing AT methods often achieve adversarial
robustness at the expense of standard generalizability (i.e., the accuracy on
natural examples) because they make a tradeoff between them. To overcome these
challenges, we propose a unique viewpoint that understands UAEs as
imperceptibly perturbed unobserved examples. Also, we find that the tradeoff
results from the separation of the distributions of adversarial examples and
natural examples. Based on these ideas, we propose a novel AT approach called
Provable Unrestricted Adversarial Training (PUAT), which can provide a target
classifier with comprehensive adversarial robustness against both UAE and RAE,
and simultaneously improve its standard generalizability. Particularly, PUAT
utilizes partially labeled data to achieve effective UAE generation by
accurately capturing the natural data distribution through a novel augmented
triple-GAN. At the same time, PUAT extends the traditional AT by introducing
the supervised loss of the target classifier into the adversarial loss and
achieves the alignment between the UAE distribution, the natural data
distribution, and the distribution learned by the classifier, with the
collaboration of the augmented triple-GAN. Finally, the solid theoretical
analysis and extensive experiments conducted on widely-used benchmarks
demonstrate the superiority of PUAT
An Ensemble Model with Ranking for Social Dialogue
Open-domain social dialogue is one of the long-standing goals of Artificial
Intelligence. This year, the Amazon Alexa Prize challenge was announced for the
first time, where real customers get to rate systems developed by leading
universities worldwide. The aim of the challenge is to converse "coherently and
engagingly with humans on popular topics for 20 minutes". We describe our Alexa
Prize system (called 'Alana') consisting of an ensemble of bots, combining
rule-based and machine learning systems, and using a contextual ranking
mechanism to choose a system response. The ranker was trained on real user
feedback received during the competition, where we address the problem of how
to train on the noisy and sparse feedback obtained during the competition.Comment: NIPS 2017 Workshop on Conversational A
Elements content in tree rings from Xi'an, China and environmental variations in the past 30 years
Using inductively coupled plasma mass spectrometry (ICP-MS) and inductively coupled plasma atomic emission spectroscopy (ICP-AES), the characteristics of chemical elements were analyzed in white poplar (Populus bonatii Levl.) and ailanthus (Ailanthus altissima (Mill.) Swingle) from three sites in the town of Xi'an, China. The results indicated that the concentration variations of Pb and Cd in tree rings were consistent with that of the environment where the trees were growing. P and Zn were translocated within tree rings to a certain degree, which led to an inaccurate pollution reconstruction. We also found that white poplar had a stronger absorptive capacity of Cd and Zn than ailanthus, which could make white poplar better as a species in environmental remediation. From this research we can see the great potential of tree rings for studying the history of different element pollution in the environment, showing that dendrochemical methods could be used as a powerful component in environmental monitoring programmes, to reconstruct past pollution history at the time when monitoring systems were not yet installed. (c) 2017 Elsevier B.V. All rights reserved
Evaluation of oral Lanzhou lamb rotavirus vaccine via passive transfusion with CD4+/CD8+ T lymphocytes
AbstractLanzhou Lamb derived Rotavirus (RV) Vaccine (namely LLR) for children is only used in China. Since there were no reports on evaluation of LLR, even the data of phase IV clinical trial, we proceed the evaluation of LLR through focusing on T-cell to investigate whether LLR could induce the potential function involving in protection as a vaccine. Four groups of nude mice were transfused with CD4+/CD8+ T-cells isolated from LLR-immunized (primed) and LLR-unimmunized (naïve) mice via intraperitonea (i.p.) respectively. Consequently, the adoption mice were challenged with mice-origin wild rotavirus EDIM (Epizootic Diarrhea of Infant Mice) by intragastric administration. Series of fecal/serum samples were collected and viral shedding, then serum IgA/IgG and secreted IgA were assayed. Compared to the mice transfused with T lymphocytes from naïve mice, the nude mice transfused with CD4+ T lymphocytes from primed mice induce fecal and serum IgA increasing more rapidly, and have a shorter duration of virus shedding too. Whereas, no significant difference in virus clearance was found between the mice transfused with CD8+ T lymphocytes isolated from primed and naïve mice. Therefore, we cleared the distinct roles of transfused CD4+/CD8+ T lymphocytes for rotavirus clearance in nude mice, that the viral clearance conducted by CD4+ T lymphocytes. Meanwhile, it has ability to help induction of LLR specific immunogenicity. Comparing with the transfusion of cell from primed and naïve mice, LLR can induce CD4+ T lymphocytes memory which is a potential index to reflect the immunogenicity and protection, while CD8+ T lymphocytes remove rotavirus by CTL with little memory ability
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