Survey of the State of the Art in Natural Language Generation: Core tasks, applications and evaluation

This paper surveys the current state of the art in Natural Language Generation (NLG), defined as the task of generating text or speech from non-linguistic input. A survey of NLG is timely in view of the changes that the field has undergone over the past decade or so, especially in relation to new (usually data-driven) methods, as well as new applications of NLG technology. This survey therefore aims to (a) give an up-to-date synthesis of research on the core tasks in NLG and the architectures adopted in which such tasks are organised; (b) highlight a number of relatively recent research topics that have arisen partly as a result of growing synergies between NLG and other areas of artificial intelligence; (c) draw attention to the challenges in NLG evaluation, relating them to similar challenges faced in other areas of Natural Language Processing, with an emphasis on different evaluation methods and the relationships between them.Comment: Published in Journal of AI Research (JAIR), volume 61, pp 75-170. 118 pages, 8 figures, 1 tabl

Proceedings of the 22nd Conference on Formal Methods in Computer-Aided Design – FMCAD 2022

The Conference on Formal Methods in Computer-Aided Design (FMCAD) is an annual conference on the theory and applications of formal methods in hardware and system verification. FMCAD provides a leading forum to researchers in academia and industry for presenting and discussing groundbreaking methods, technologies, theoretical results, and tools for reasoning formally about computing systems. FMCAD covers formal aspects of computer-aided system design including verification, specification, synthesis, and testing

The cloudUPDRS app: a medical device for the clinical assessment of Parkinson's Disease

Parkinson's Disease is a neurological condition distinguished by characteristic motor symptoms including tremor and slowness of movement. To enable the frequent assessment of PD patients, this paper introduces the cloudUPDRS app, a Class I medical device that is an active transient non-invasive instrument, certified by the Medicines and Healthcare products Regulatory Agency in the UK. The app follows closely Part III of the Unified Parkinson's Disease Rating Scale which is the most commonly used protocol in the clinical study of PD; can be used by patients and their carers at home or in the community unsupervised; and, requires the user to perform a sequence of iterated movements which are recorded by the phone sensors. The cloudUPDRS system addresses two key challenges towards meeting essential consistency and efficiency requirements, namely: (i) How to ensure high-quality data collection especially considering the unsupervised nature of the test, in particular, how to achieve firm user adherence to the prescribed movements; and (ii) How to reduce test duration from approximately 25 minutes typically required by an experienced patient, to below 4 minutes, a threshold identified as critical to obtain significant improvements in clinical compliance. To address the former, we combine a bespoke design of the user experience tailored so as to constrain context, with a deep learning approach based on Recurrent Convolutional Neural Networks, to identify failures to follow the movement protocol. We address the latter by developing a machine learning approach to personalize assessments by selecting those elements of the test that most closely match individual symptom profiles and thus offer the highest inferential power, hence closely estimating the patent's overall score

Inquiries into words, constraints and contexts : Festschrift in the honour of Kimmo Koskenniemi on his 60th birthday

Peer reviewe

Corpus-based unit selection for natural-sounding speech synthesis

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2003.Includes bibliographical references (p. 179-196).This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Speech synthesis is an automatic encoding process carried out by machine through which symbols conveying linguistic information are converted into an acoustic waveform. In the past decade or so, a recent trend toward a non-parametric, corpus-based approach has focused on using real human speech as source material for producing novel natural-sounding speech. This work proposes a communication-theoretic formulation in which unit selection is a noisy channel through which an input sequence of symbols passes and an output sequence, possibly corrupted due to the coverage limits of the corpus, emerges. The penalty of approximation is quantified by substitution and concatenation costs which grade what unit contexts are interchangeable and where concatenations are not perceivable. These costs are semi-automatically derived from data and are found to agree with acoustic-phonetic knowledge. The implementation is based on a finite-state transducer (FST) representation that has been successfully used in speech and language processing applications including speech recognition. A proposed constraint kernel topology connects all units in the corpus with associated substitution and concatenation costs and enables an efficient Viterbi search that operates with low latency and scales to large corpora. An A* search can be applied in a second, rescoring pass to incorporate finer acoustic modelling. Extensions to this FST-based search include hierarchical and paralinguistic modelling. The search can also be used in an iterative feedback loop to record new utterances to enhance corpus coverage. This speech synthesis framework has been deployed across various domains and languages in many voices, a testament to its flexibility and rapid prototyping capability.(cont.) Experimental subjects completing tasks in a given air travel planning scenario by interacting in real time with a spoken dialogue system over the telephone have found the system "easiest to understand" out of eight competing systems. In more detailed listening evaluations, subjective opinions garnered from human participants are found to be correlated with objective measures calculable by machine.by Jon Rong-Wei Yi.Ph.D

Becoming Human with Humanoid

Nowadays, our expectations of robots have been significantly increases. The robot, which was initially only doing simple jobs, is now expected to be smarter and more dynamic. People want a robot that resembles a human (humanoid) has and has emotional intelligence that can perform action-reaction interactions. This book consists of two sections. The first section focuses on emotional intelligence, while the second section discusses the control of robotics. The contents of the book reveal the outcomes of research conducted by scholars in robotics fields to accommodate needs of society and industry