3,196 research outputs found
Stochastic Language Generation in Dialogue using Recurrent Neural Networks with Convolutional Sentence Reranking
The natural language generation (NLG) component of a spoken dialogue system
(SDS) usually needs a substantial amount of handcrafting or a well-labeled
dataset to be trained on. These limitations add significantly to development
costs and make cross-domain, multi-lingual dialogue systems intractable.
Moreover, human languages are context-aware. The most natural response should
be directly learned from data rather than depending on predefined syntaxes or
rules. This paper presents a statistical language generator based on a joint
recurrent and convolutional neural network structure which can be trained on
dialogue act-utterance pairs without any semantic alignments or predefined
grammar trees. Objective metrics suggest that this new model outperforms
previous methods under the same experimental conditions. Results of an
evaluation by human judges indicate that it produces not only high quality but
linguistically varied utterances which are preferred compared to n-gram and
rule-based systems.Comment: To be appear in SigDial 201
Dephasing in the electronic Mach-Zehnder interferometer at filling factor 2
We propose a simple physical model which describes dephasing in the
electronic Mach-Zehnder interferometer at filling factor 2. This model explains
very recent experimental results, such as the unusual lobe-type structure in
the visibility of Aharonov-Bohm oscillations, phase rigidity, and the asymmetry
of the visibility as a function of transparencies of quantum point contacts.
According to our model, dephasing in the interferometer originates from strong
Coulomb interaction at the edge of two-dimensional electron gas. The long-range
character of the interaction leads to a separation of the spectrum of edge
excitations on slow and fast mode. These modes are excited by electron
tunneling and carry away the phase information. The new energy scale associated
with the slow mode determines the temperature dependence of the visibility and
the period of its oscillations as a function of voltage bias. Moreover, the
variation of the lobe structure from one experiment to another is explained by
specific charging effects, which are different in all experiments. We propose
to use a strongly asymmetric Mach-Zehnder interferometer with one arm being
much shorter than the other for the spectroscopy of quantum Hall edge states.Comment: 14 pages, 11 figure
On the Self-Repair of WS2/a-C Tribocoating
This study investigates the self-healing capacity of a WS2/amorphous carbon (a-C) tribocoating. It is found that prenotches up to 45 µm wide in the WS2/a-C coating surface can be completely healed under the stimulus of sliding operations. The in situ tribotest of 100, 500, 2000, and 6000 laps confirms a dynamic filling of tribofilms that patch the voids and prenotched damages. The stabilized coefficient of friction (CoF) remains at an ultralow value down to 0.02, independent of the prenotched damage at the top of coating. The sites of notched damage in fact act as lubricant reservoirs to accumulate the otherwise “wasted” debris, which are restored as a superlubricant by the sliding operation. High resolution transmission electron microscopy reveals that WS2 (002) nanoplatelets in the healed notch are parallel to the top coating surface but conformal to the coating/notch interface. The patchy tribofilm holds excellent promise for the self-repair of damages in the field of tribology
Policy committee for adaptation in multi-domain spoken dialogue systems
Moving from limited-domain dialogue systems to open domain dialogue systems raises a number of challenges. One of them is the ability of the system to utilise small amounts of data from disparate domains to build a dialogue manager policy. Previous work has focused on using data from different domains to adapt a generic policy to a specific domain. Inspired by Bayesian committee machines, this paper proposes the use of a committee of dialogue policies. The results show that such a model is particularly beneficial for adaptation in multi-domain dialogue systems. The use of this model significantly improves performance compared to a single policy baseline, as confirmed by the performed real-user trial. This is the first time a dialogue policy has been trained on multiple domains on-line in interaction with real users.The research leading to this work was funded by the EPSRC grant EP/M018946/1 ”Open Domain Statistical Spoken Dialogue Systems”.This is the author accepted manuscript. The final version is available from IEEE via http://dx.doi.org/10.1109/ASRU.2015.740487
Policy committee for adaptation in multi-domain spoken dialogue systems
Moving from limited-domain dialogue systems to open domain dialogue systems raises a number of challenges. One of them is the ability of the system to utilise small amounts of data from disparate domains to build a dialogue manager policy. Previous work has focused on using data from different domains to adapt a generic policy to a specific domain. Inspired by Bayesian committee machines, this paper proposes the use of a committee of dialogue policies. The results show that such a model is particularly beneficial for adaptation in multi-domain dialogue systems. The use of this model significantly improves performance compared to a single policy baseline, as confirmed by the performed real-user trial. This is the first time a dialogue policy has been trained on multiple domains on-line in interaction with real users.The research leading to this work was funded by the EPSRC grant EP/M018946/1 ”Open Domain Statistical Spoken Dialogue Systems”.This is the author accepted manuscript. The final version is available from IEEE via http://dx.doi.org/10.1109/ASRU.2015.740487
Influence of random roughness on the Casimir force at small separations
The influence of random surface roughness of Au films on the Casimir force is
explored with atomic force microscopy in the plate-sphere geometry. The
experimental results are compared to theoretical predictions for separations
ranging between 20 and 200 nm. The optical response and roughness of the Au
films were measured and used as input in theoretical predictions. It is found
that at separations below 100 nm, the roughness effect is manifested through a
strong deviation from the normal scaling of the force with separation distance.
Moreover, deviations from theoretical predictions based on perturbation theory
can be larger than 100%.Comment: 18, 5 figure
Corrigendum to “On the S/W stoichiometry and triboperformance of WS<sub>x</sub>C(H) coatings deposited by magnetron sputtering” [Surface and Coatings Technology 365 (2019) 41-51]
The authors regret that in Fig. 1b some data points of s/w ratio were not correctly aligned with the x-axis of target-to-substrate distance. The corrected Fig. 1b is shown below. [Figure presented] The authors would like to apologise for any inconvenience caused
Multi-domain neural network language generation for spoken dialogue systems
Moving from limited-domain natural language generation (NLG) to open domain
is difficult because the number of semantic input combinations grows
exponentially with the number of domains. Therefore, it is important to
leverage existing resources and exploit similarities between domains to
facilitate domain adaptation. In this paper, we propose a procedure to train
multi-domain, Recurrent Neural Network-based (RNN) language generators via
multiple adaptation steps. In this procedure, a model is first trained on
counterfeited data synthesised from an out-of-domain dataset, and then fine
tuned on a small set of in-domain utterances with a discriminative objective
function. Corpus-based evaluation results show that the proposed procedure can
achieve competitive performance in terms of BLEU score and slot error rate
while significantly reducing the data needed to train generators in new, unseen
domains. In subjective testing, human judges confirm that the procedure greatly
improves generator performance when only a small amount of data is available in
the domain.Toshiba Research Europe Ltd.This is the accepted manuscript. It is currently embargoed pending publication
Scaling of the Fano effect of the in-plane Fe-As phonon and the superconducting critical temperature in BaKFeAs
By means of infrared spectroscopy we determine the temperature-doping phase
diagram of the Fano effect for the in-plane Fe-As stretching mode in
BaKFeAs. The Fano parameter , which is a
measure of the phonon coupling to the electronic particle-hole continuum, shows
a remarkable sensitivity to the magnetic/structural orderings at low
temperatures. More strikingly, at elevated temperatures in the
paramagnetic/tetragonal state we find a linear correlation between and
the superconducting critical temperature . Based on theoretical
calculations and symmetry considerations, we identify the relevant interband
transitions that are coupled to the Fe-As mode. In particular, we show that a
sizable orbital component at the Fermi level is fundamental for the Fano
effect and possibly also for the superconducting pairing.Comment: Supplemental materials are available upon reques
Dialogue manager domain adaptation using Gaussian process reinforcement learning
Spoken dialogue systems allow humans to interact with machines using natural
speech. As such, they have many benefits. By using speech as the primary
communication medium, a computer interface can facilitate swift, human-like
acquisition of information. In recent years, speech interfaces have become ever
more popular, as is evident from the rise of personal assistants such as Siri,
Google Now, Cortana and Amazon Alexa. Recently, data-driven machine learning
methods have been applied to dialogue modelling and the results achieved for
limited-domain applications are comparable to or outperform traditional
approaches. Methods based on Gaussian processes are particularly effective as
they enable good models to be estimated from limited training data.
Furthermore, they provide an explicit estimate of the uncertainty which is
particularly useful for reinforcement learning. This article explores the
additional steps that are necessary to extend these methods to model multiple
dialogue domains. We show that Gaussian process reinforcement learning is an
elegant framework that naturally supports a range of methods, including prior
knowledge, Bayesian committee machines and multi-agent learning, for
facilitating extensible and adaptable dialogue systems.Engineering and Physical Sciences Research Council (Grant ID: EP/M018946/1 ”Open Domain Statistical Spoken Dialogue Systems”
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