ML Project F: Delivering a Natural Language Programming-driven Question-Answering research repository

This tender consists of seven interrelated procurements. The applicant may apply for all seven Projects A-G, but successful applicants will only be contracted for Project A alone, any combination of Projects B-G, or all Projects B-G.

For full details, including links to relevant documents and information on any Questions and Answers, visit:

https://grdc.com.au/research/applying-and-reporting/current-procurement/open-tenders

Background:

There is an opportunity to extract greater value from text-encoded information in resources such as GRDC reports and other publications relevant to Australian grain growers. Much grains R&D output is only accessible in text format. Taking GRDC as an example, much R&D output is only captured in project reports and communications – such information is not always easily discovered or synthesised. Machine learning (ML) could be applied to text, through Natural Language Programming.

Such a ML solution could be packaged into a Question-Answering (QA) tool like those deployed in public and private customer service settings. The QA tool would be able to parse questions posed by growers and then draw upon machine-learnt text to provide suitable responses, improving information-delivery to growers.

Evaluation Criteria, Outcomes and Outputs:

Applications must address the evaluation criteria, outcomes, and outputs relevant to the specific procurement being applied for.

For details visit:

https://grdc.com.au/research/applying-and-reporting/current-procurement/open-tenders