We present a conversational model to apprise users with limited access to computational resources about water quality and real-time accessibility for a given location. We used natural language understanding through neural embedding driven approaches. This was integrated with a chatbot interface to accept user queries and decide on action output based on entity recognition from such input query and online information from standard databases and governmental and non-governmental resources. We present results of attempts made for some South African use cases, and demonstrate utility for information search and dissemination at a local level.
Reference:
Lourens, R.L. et al. 2018. Water quality information dissemination at real-time in South Africa using language modelling. Machine Learning for the Developing World (ML4D) Workshop, part of the 23rd Conference on Neural Information Processing Systems (NIPS 2018), 8 December 2018, Palais des Congrès de Montréal, Montréal, Canada
Lourens, R. L., Patra, A., Hassim, L., Sima, F., Moodley, A., & Sharma, P. (2018). Water quality information dissemination at real-time in South Africa using language modelling. http://hdl.handle.net/10204/10893
Lourens, Roger L, A Patra, Luqmaan Hassim, Faheem Sima, Avashlin Moodley, and P Sharma. "Water quality information dissemination at real-time in South Africa using language modelling." (2018): http://hdl.handle.net/10204/10893
Lourens RL, Patra A, Hassim L, Sima F, Moodley A, Sharma P, Water quality information dissemination at real-time in South Africa using language modelling; 2018. http://hdl.handle.net/10204/10893 .
Paper presented at the Machine Learning for the Developing World (ML4D) Workshop, part of the 23rd Conference on Neural Information Processing Systems (NIPS 2018), 8 December 2018, Palais des Congrès de Montréal, Montréal, Canada
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