Natural Language Generation for Low-resource Domains

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

This multi-disciplinary project will have impacts beyond academia.

*Privacy-preserving personal assistants/NLG systems*

As the use of personal assistants grows globally, the need for privacy-preserving approaches to NLG increases. Most industries handle sensitive information such as personal and private data and although data scientists strive to anonymise data records, sophisticated de-anonymisation approaches can pose a threat not only to privacy but also current legislation. Therefore, the impact of innovative approaches that respect privacy and ethical constraints will be enormous.

*Increase productivity by automating the descriptions of products*

Additionally, industries that rely on online presence will benefit from approaches that automatically generate text summaries from structured data, such as descriptions of products and services, since these approaches can increase productivity by automating repetitive and laborious tasks. In addition, diversity-enriched NLG approaches can make the content of automatically generated summaries more interesting and less repetitive, and hence increase the overall user experience.

*Automatic narrative and report generation*

Narrative generation from data, such as automatic news story generation, will benefit from more natural NLG approaches that offer variability and empathy as stories can be enriched with emotion, and interesting non-repetitive text. Business intelligence and analytics reporting will also benefit from the approaches developed here, as privacy is integral when communicating data and insights. In addition, NLG has been shown to enhance decision making support [5].

*Support Health and Well-being*

AI-powered personal assistants, such as the Alli-chat developed by our project partner, have started to become prevalent in health support [1]. It can also be preferable for supporting specific groups such as younger people for stigma-attached conditions such as mental health. Younger people are particularly less likely to seek help when facing mental health challenges [4], therefore, it is of vital importance to create a safe space for younger people that empowers them to seek private advice and information regarding mental well-being which can be achieved through trusted, privacy-sensitive, empathy-enriched personal assistants. Promotion of mental well-being, management, and prevention of mental health illnesses has been indeed identified as a core priority of the World Health Organisation's mental health action plan 2013-2020 [2] as well as in NHS's "Five Years Forward View" for mental health [3].

References:
[1] https://www.healthcareitnews.com/news/special-report-ai-voice-assistants-making-impact-healthcare
[2] https://www.who.int/mental_health/publications/action_plan/en/
[3] https://www.england.nhs.uk/wp-content/uploads/2014/10/5yfv-web.pdf
[4] Marcus, M. A., Westra, H. A., Eastwood, J. D., Barnes, K. L., & Mobilizing Minds Research Group (2012). What are young adults saying about mental health? An analysis of Internet blogs. Journal of medical Internet research, 14(1), e17. doi:10.2196/jmir.1868
[5] Gkatzia et al. (2017). Data-to-Text Generation Improves Decision-Making Under Uncertainty. IEEE Computational Intelligence Magazine, Special Issue on Natural Language Generation with Computational Intelligence.