803 research outputs found
Scaling Recurrent Neural Network Language Models
This paper investigates the scaling properties of Recurrent Neural Network
Language Models (RNNLMs). We discuss how to train very large RNNs on GPUs and
address the questions of how RNNLMs scale with respect to model size,
training-set size, computational costs and memory. Our analysis shows that
despite being more costly to train, RNNLMs obtain much lower perplexities on
standard benchmarks than n-gram models. We train the largest known RNNs and
present relative word error rates gains of 18% on an ASR task. We also present
the new lowest perplexities on the recently released billion word language
modelling benchmark, 1 BLEU point gain on machine translation and a 17%
relative hit rate gain in word prediction
Families and work: revisiting barriers to employment
"In recent years, considerable effort has been put into supporting parents to make the transition
into work. This study was commissioned by the Department for Work and Pensions (DWP) to explore whether these incentives were helping parents to overcome the barriers known to impede their engagement in the formal labour market.
The report is based on fieldwork conducted in 2009. However, the concluding chapter considers the significance of the findings in light of proposals for the introduction of the Universal Credit and other reforms of the tax and benefit systems proposed by the Coalition Government." - Page 1
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Dynamic Reinforcement Driven Error Propagation Networks with Application to Game Playing
This paper discusses the problem of the reinforcement driven learning of a response to a time varying sequence. The problem has three parts: the adaptation of internal parameters to model complex mappings; the ability of the architecture to represent time varying input; and the problem of credit assignment with unknown delays between the input, output and reinforcement signals. The method developed in this paper is based on a connectionist network trained using the error propagation algorithm with internal feedback. The network is viewed both as a context dependent predictor of the reinforcement signal and as a means of temporal credit assignment. Several architectures for these networks are discussed and insight into the implementation problems is gained by an application to the game of noughts and crosses
A Motivation Scaffold to Improve the Learning Engagement of Students
Teachers who work with young adolescents know that motivating and maintaining their interest in classroom-based learning is a major challenge.
This study seeks to address this issue and is an examination of the use of a motivational scaffold to assist a cohort of Year Nine students to take greater responsibility for their learning through direct and authentic learning experiences outside the classroom
Increasing Life Effectiveness
The iPod, more than any other device, is indicative of the times in which we live. It provides entertainment and information at the click of a wheel, whenever, and wherever we want it. The iPod is tool of choice for many of the current generation of youth who fill their days with electronic devices, computer games, Youtube, Myspace, Facebook and talking to friends on MSN. These youth have been referred to as the iGeneration, or Google Generation; whatever you choose to call them, they are the young people in our schools.
Life is not simple for many of these students. They
are growing up in a world vastly different to that
of their parents. Today’s world features “cultural
pluralism, increased anxiety about personal and
environmental risks, precarious employment,
rampant consumerism, the information deluge,
greater individualisation and increased instability in
families” (Hughes, 2007).
Within this quickly changing world, there is a
need for students to develop the capacity to cope
with their ever-changing environment. They need
to be resilient. Outdoor education activities have
been proposed as one way of increasing a person’s
resilience through increasing ‘Life Effectiveness’
skills. These skills equip students to handle the
demands of life and impact a person’s capacity to
adapt, survive, and thrive (Neill, 2008). They will
enhance a person’s resilience and their sense of
wellbeing
A Review of Evaluation Practices of Gesture Generation in Embodied Conversational Agents
Embodied Conversational Agents (ECA) take on different forms, including
virtual avatars or physical agents, such as a humanoid robot. ECAs are often
designed to produce nonverbal behaviour to complement or enhance its verbal
communication. One form of nonverbal behaviour is co-speech gesturing, which
involves movements that the agent makes with its arms and hands that is paired
with verbal communication. Co-speech gestures for ECAs can be created using
different generation methods, such as rule-based and data-driven processes.
However, reports on gesture generation methods use a variety of evaluation
measures, which hinders comparison. To address this, we conducted a systematic
review on co-speech gesture generation methods for iconic, metaphoric, deictic
or beat gestures, including their evaluation methods. We reviewed 22 studies
that had an ECA with a human-like upper body that used co-speech gesturing in a
social human-agent interaction, including a user study to evaluate its
performance. We found most studies used a within-subject design and relied on a
form of subjective evaluation, but lacked a systematic approach. Overall,
methodological quality was low-to-moderate and few systematic conclusions could
be drawn. We argue that the field requires rigorous and uniform tools for the
evaluation of co-speech gesture systems. We have proposed recommendations for
future empirical evaluation, including standardised phrases and test scenarios
to test generative models. We have proposed a research checklist that can be
used to report relevant information for the evaluation of generative models as
well as to evaluate co-speech gesture use.Comment: 9 page
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