Journal
APPLIED SCIENCES-BASEL
Volume 12, Issue 22, Pages -Publisher
MDPI
DOI: 10.3390/app122211830
Keywords
human-machine interaction; natural language processing; semantic parsing; agricultural measurement and control; natural user interface
Categories
Funding
- Anhui agricultural ecological and environmental protection and quality safety industrial technology system grant [22803029]
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This paper proposes a pipeline for natural language human-machine interaction interface in agricultural measurement and control platforms. Experimental results show that the proposed model performs well with reasonable response time.
The human-machine interaction of existing agricultural measurement and control platforms lacks user-friendliness and requires manual operation by trained professionals. The recent development of natural language processing technology may bring some interesting changes. We propose a pipeline for building a natural language human-machine interaction interface to provide a better interaction for agricultural measurement and control platforms. Our construction process uses a new method of collecting training data based on the dynamic tuple language framework to synthesize natural language commands entered by the user into structured AOM statements (Action-Object-Member). To construct a mapping of the human-machine interface from natural language commands to AOM invocations, we propose an end-to-end framework that uses a special mask mechanism to improve the BERT-based Seq2Seq model to capture global sequence relations. Experimental results of data collection methods and NL2AOM demonstrate that our pipeline has good performance and a reasonable response time. Finally, we developed desktop and mobile platform applications based on the proposed model and used them in real agricultural scenarios.
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