ARTD6084 DISPLAY:INTERPRETING THE LANGAUGE OF DESIGN

Please contact the tutor Dr Jonathan Faiers for any further information j.faiers@soton.ac.uk

The langauge of tourism : perfecting soft skills in a flat world

India is Australia's 11th-biggest inbound tourism market, bringing in 148,200 visitors who spent $867 million last year, but by 2020 officials say that could reach 300,000 visitors spending $2.3 billion.
Delhi and Mumbai have been targeted by Australia because they have an emerging middle class and India's highest concentration of affluent households.
The Minister for Tourism, Martin Ferguson, unveiled an India 2020 strategic plan last month at the annual Australian Tourism Exchange in Perth, the largest travel trade show in the southern hemisphere. "We have put a huge effort into attracting tourists from China recently and the next cab off the rank is India," he said.
The plan means that Tourism Australia's "There's Nothing Like Australia" campaign will be rolled out in Delhi and Mumbai and there will be extensive advertising on TV and digital channels as well as print.

Continuity and change in family engagement in home learning activities across the early years

This research utilised data from The Longitudinal Study of Australian Children and explored continuity and change in parental engagement in home learning activities with young children. The findings indicated a decrease over time in parental engagement with children, from age to 2-3 years to 6-7 years. Rate of decrease impacted negatively on learning outcomes for language and literacy, and mathematical thinking, in the early years of school, when children were aged 6-7 years. Shared reading with children and interactions around everyday home activities and play, in which children and parents participate together, impact on children's later development.

Word study: synonyms and antonyms

This paper contains an outline of study in langauge development of synonyms and antonyms for hearing impaired children

World within no more

The tiny Kelabit community of the central highlands of Borneo was isolated and unconnected with the world outside of the highlands until the middle of the 20th Century. Their response to contact has been to embrace education and seek to understand the language and ways of outsiders. They have achieved high rates of tertiary qualifications and Kelabits now hold major professional, business and government positions in Sarawak and Malaysia disproportionately to the size of their population. The consequence has been a loss of cultural practices and langauge and now they are concerned that they are losing their distinct Kelabit identity.

This film was made as part of the development of a museum proposal and to identify the significant intangible cultural heritage through which the Kelabits wish to preserve and express their identity.

SEQUENTIAL DECISIONS AND PREDICTIONS IN NATURAL LANGUAGE PROCESSING

Natural language processing has achieved great success in a wide range of ap- plications, producing both commercial language services and open-source language tools. However, most methods take a static or batch approach, assuming that the model has all information it needs and makes a one-time prediction. In this disser- tation, we study dynamic problems where the input comes in a sequence instead of all at once, and the output must be produced while the input is arriving. In these problems, predictions are often made based only on partial information. We see this dynamic setting in many real-time, interactive applications. These problems usually involve a trade-off between the amount of input received (cost) and the quality of the output prediction (accuracy). Therefore, the evaluation considers both objectives (e.g., plotting a Pareto curve). Our goal is to develop a formal understanding of sequential prediction and decision-making problems in natural language processing and to propose efficient solutions. Toward this end, we present meta-algorithms that take an existent batch model and produce a dynamic model to handle sequential inputs and outputs. Webuild our framework upon theories of Markov Decision Process (MDP), which allows learning to trade off competing objectives in a principled way. The main machine learning techniques we use are from imitation learning and reinforcement learning, and we advance current techniques to tackle problems arising in our settings. We evaluate our algorithm on a variety of applications, including dependency parsing, machine translation, and question answering. We show that our approach achieves a better cost-accuracy trade-off than the batch approach and heuristic-based decision- making approaches. We first propose a general framework for cost-sensitive prediction, where dif- ferent parts of the input come at different costs. We formulate a decision-making process that selects pieces of the input sequentially, and the selection is adaptive to each instance. Our approach is evaluated on both standard classification tasks and a structured prediction task (dependency parsing). We show that it achieves similar prediction quality to methods that use all input, while inducing a much smaller cost. Next, we extend the framework to problems where the input is revealed incremen- tally in a fixed order. We study two applications: simultaneous machine translation and quiz bowl (incremental text classification). We discuss challenges in this set- ting and show that adding domain knowledge eases the decision-making problem. A central theme throughout the chapters is an MDP formulation of a challenging problem with sequential input/output and trade-off decisions, accompanied by a learning algorithm that solves the MDP.