Building Natural Language Interfaces Using Natural Language Understanding and Generation: A Case Study on Human–Machine Interaction in Agriculture

Abstract

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.