Transfer Learning for Speech and Language Processing

Transfer learning is a vital technique that generalizes models trained for one setting or task to other settings or tasks. For example in speech recognition, an acoustic model trained for one language can be used to recognize speech in another language, with little or no re-training data. Transfer learning is closely related to multi-task learning (cross-lingual vs. multilingual), and is traditionally studied in the name of `model adaptation'. Recent advance in deep learning shows that transfer learning becomes much easier and more effective with high-level abstract features learned by deep models, and the `transfer' can be conducted not only between data distributions and data types, but also between model structures (e.g., shallow nets and deep nets) or even model types (e.g., Bayesian models and neural models). This review paper summarizes some recent prominent research towards this direction, particularly for speech and language processing. We also report some results from our group and highlight the potential of this very interesting research field.Comment: 13 pages, APSIPA 201

Feasibility Study: Development and Demonstration of Virtual Reality Simulation Training for the BHPB Olympic Dam Site Inductions

This report presents the findings of the project ―Feasibility Study: Development and Demonstration of Virtual Reality Simulation Training for the BHPB Olympic Dam Site Inductions.‖ The project was a collaborative exercise between the University of New South Wales (UNSW) - School of Mining Engineering, the University of Adelaide - Australian Centre for Visual Technologies, BHPB Olympic Dam Expansion, RESA, TAFESA and Skills DMC. The project Chief Investigators were Dr Phillip Stothard (UNSW) and Prof Anton van den Hengel (University of Adelaide).The project was a pilot study research project that looked into the feasibility of developing interactive virtual reality simulations for mine site inductions in the hard rock industry. Many simulations have been successfully implemented into the coal industry and the aim was to build a pilot module that looked at a high risk environment on a surface mine that would also have application to the wider construction industry and other heavy industries. The project collaborators came together as a group of parties interested in virtual reality simulation. The research and development was led by UNSW and University of Adelaide. Invaluable input was provided by the collaborators. The project had a value of $431, 306. Of which$208,563 was in cash and $222,743 was in kind. The budget was fully expended during the course of the project. The subject area of the project was ̳Working at Heights‘ and this was chosen because it is a high risk area. Substantial documentation, mining industry input and effort was placed on building the five sub-modules that form the Working at Heights module. The outcome is a high quality visualisation of an area of the Olympic Dam Mine Site. This high quality visualisation is enhanced by the inclusion of interaction within the module that requires the user to interrogate data within the site and to assess and understand issues that arise when working at heights in relation ladders, scaffolding, open excavations and elevated work platforms. Much project emphasis and time was placed on producing the 3D model. Also, as much information as possible was placed into the module itself as this was to be a pilot example to show to the Olympic Dam Expansion Project Team. The module allows users to interact with Safety Documentation and equipment and procedures that they would encounter on sit

Rare words in text summarization

Automatic text summarization is a difficult task, which involves a good understanding of an input text to produce fluent, brief and vast summary. The usage of text summarization models can vary from legal document summarization to news summarization. The model should be able to understand where important information is located to produce a good summary. However, infrequently used or rare words might limit model’s understanding of an input text, as the model might ignore such words or put less attention on them. Another issue is that the model accepts only a limited amount of tokens (words) of an input text, which might contain redundant information or not including important information as it is located further in the text. To address the problem of rare words, we have proposed a modification to the attention mechanism of the transformer model with pointer-generator layer, where attention mechanism receives frequency information for each word, which helps to boost rare words. Additionally, our proposed supervised learning model uses the hybrid approach incorporating both extractive and abstractive elements, to include more important information for the abstractive model in a news summarization task. We have designed experiments involving a combination of six different hybrid models with varying input text sizes (measured as tokens) to test our proposed model. Four wellknown datasets specific to news articles were used in this work: CNN/DM, XSum, Gigaword and DUC 2004 Task 1. Our results were compared using the well-known ROUGE metric. Our best model achieved R-1 score of 38.22, R-2 score of 15.07 and R-L score of 35.79, outperforming three existing models by several ROUGE points.Master of Science in Applied Computer Scienc