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_xC(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 Ba1−x_{1-x}Kx_{x}Fe2_{2}As2_{2}

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 Ba$_{1-x}$K$_{x}$Fe$_{2}$As$_{2}$. The Fano parameter $1/q^2$, 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 $1/q^2$ and the superconducting critical temperature $T_c$. 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 $xy$ 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”