Natural Language Generation for Low-resource Domains

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It is expected that by 2021, Artificial Intelligence (AI) based dialogue systems such as Amazon's Alexa and Apple's Siri will exceed the earth's population [1]. Such interactive technology products have already become prevalent in many aspects of everyday life, offering support for decision making, education, and health as well as entertainment, by effectively communicating in natural language to answer questions, describe or summarise data, and assist in multiple areas. To develop such systems, however, AI requires access to vast amounts of examples of dialogues, which can (1) be hard to attain in many domains due to unavailability; and (2) pose privacy concerns, impacting user uptake [2]. Current response generation techniques are heavily based on pre-specified templates that limit language coverage. Generating naturally fluent responses is heavily dependant on example dialogues, that are scarcely available in many domains. To address these interlinked challenges, the project will firstly develop natural language generation techniques that are able to learn from limited resources by reusing the knowledge learnt in other data-rich domains, similar to the way the human brain learns new skills efficiently by building on prior knowledge. Secondly, we will develop novel privacy-preserving AI methods to address the second important challenge, and eliminate the risk for de-anonymisation of data. Although recent advances in understanding natural language have made it possible to accurately predict the meaning of users' utterances and hence accurately inform the personal assistants' actions, responding in natural language remains a bottleneck for the current generation of dialogue systems and personal assistants. As more interactive systems generating natural language become available, the need for natural variability and novelty in the generated text becomes significant in order to increase end-user satisfaction and engagement. Therefore the project will also develop AI approaches that generate text that shows novelty and variability for enriching the word choice while keeping the semantics of the generated text unchanged. Finally, many real-world applications such as personal assistants (and also chatbots and social robots) that support health or education, will benefit from generated responses that show empathy and adapt to users' psychological state. This requires a deep understanding of emotions from text, therefore, this project will, for the first time, develop and integrate innovative, natural language 'concept' based approaches, to understand user emotions from underlying text, and inform novel text generation approaches. Practical case studies provided by our industrial partners will be used to validate our developed AI approaches, throughout this ambitious project.References:[1] https://ovum.informa.com/resources/product-content/virtual-digital-assistants-to-overtake-world-population-by-2021 [2] https://www.independent.co.uk/life-style/gadgets-and-tech/news/amazon-alexa-echo-listening-spy-security-a8865056.html