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

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

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

From high-mass starless cores to high-mass protostellar objects

Aims: Our aim is to understand the evolutionary sequence of high-mass star formation from the earliest evolutionary stage of high-mass starless cores, via high-mass cores with embedded low- to intermediate-mass objects, to finally high-mass protostellar objects. Methods: Herschel far-infrared PACS and SPIRE observations are combined with existing data at longer and shorter wavelengths to characterize the spectral and physical evolution of massive star-forming regions. Results: The new Herschel images spectacularly show the evolution of the youngest and cold high-mass star-forming regions from mid-infrared shadows on the Wien-side of the spectral energy distribution (SED), via structures almost lost in the background emission around 100mum, to strong emission sources at the Rayleigh-Jeans tail. Fits of the SEDs for four exemplary regions covering evolutionary stages from high-mass starless cores to high-mass protostellar objects reveal that the youngest regions can be fitted by single-component black-bodies with temperatures on the order of 17K. More evolved regions show mid-infrared excess emission from an additional warmer component, which however barely contributes to the total luminosities for the youngest regions. Exceptionally low values of the ratio between bolometric and submm luminosity additionally support the youth of the infrared-dark sources. Conclusions: The Herschel observations reveal the spectral and physical properties of young high-mass star-forming regions in detail. The data clearly outline the evolutionary sequence in the images and SEDs. Future work on larger samples as well as incorporating full radiative transfer calculations will characterize the physical nature at the onset of massive star formation in even more depth.Comment: 4 pages, A&A Herschel special issu

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”

Trusting numbers: uncertainty and the pathology laboratory

The document attached has been archived with permission from the editor of the Medical Journal of Australia. An external link to the publisher’s copy is included.T Paul Hutchinso

Absolute Frequency Measurements of the Hg^+ and Ca Optical Clock Transitions with a Femtosecond Laser

The frequency comb created by a femtosecond mode-locked laser and a microstructured fiber is used to phase coherently measure the frequencies of both the Hg^+ and Ca optical standards with respect to the SI second as realized at NIST. We find the transition frequencies to be f_Hg=1 064 721 609 899 143(10) Hz and f_Ca=455 986 240 494 158(26) Hz, respectively. In addition to the unprecedented precision demonstrated here, this work is the precursor to all-optical atomic clocks based on the Hg^+ and Ca standards. Furthermore, when combined with previous measurements, we find no time variations of these atomic frequencies within the uncertainties of |(df_Ca/dt)/f_Ca| < 8 x 10^{-14} yr^{-1}, and |(df_Hg/dt)/f_Hg|< 30 x 10^{-14} yr^{-1}.Comment: 6 pages, including 4 figures. RevTex 4. Submitted to Phys. Rev. Let