# Generating Process Models by Interacting with Chatbots--A Literature Review
## 5 Reporting of the Systematic Literature Review
### Generating Process Models from Natural Language Text Input (RQ1)
By combining collaborative techniques with process mining, study S6, 'A Case Study on Designing Business Processes Based on Collaborative and Mining Approaches' [17], provides practical insights, demonstrated through a case study on the Ghana cocoa supply chain. Easing the transition from textual requirements to formal process models, study S8, 'Generating BPMN Diagram from Textual Requirements' [19] introduces automation through NLP. By interpreting textual requirements and generating corresponding BPMN diagrams, this study facilitates a seamless visualization of business processes. Ensuring accuracy in process element identification poses a challenge addressed by study S9, 'A Visual Approach for Identification and Annotation of Business Process Elements in Process Descriptions' [20]. Proposing a user-interactive method, study S9 enhances accuracy by employing visual tools for identifying and annotating process elements more precisely. Efforts towards automation persist in study S10, 'Extracting Declarative Process Models from Natural Language' [21], where the manual extraction of declarative process models is transformed into an automated process using tailored NLP techniques. By identifying  
Figure 4: Distribution of the used methods for RQ1. activities and constraints, this methodology generates declarative process models efficiently from textual data. For identifying business process elements, study S11, 'A Semi-automatic Approach to Identify Business Process Elements in Natural Language Texts' [22], combines manual and automated approaches. By utilizing empirical mapping rules and NLP, this method semi-automatically identifies process elements from text, balancing efficiency with accuracy. Study S12, 'Supporting the Process of Learning and Teaching Process Models' [23], introduces evaluation tools to aid process modelers creating accurate process models. By providing feedback on the correctness of process models, this approach enriches the learning experience, facilitating better comprehension and retention. Addressing the challenge of process model discovery from unstructured text, study S13, 'Automatic Process Model Discovery from Textual Methodologies' [24], focuses on verb semantics. By leveraging NLP to mine process models, this study offers insights into structuring process elements from textual data. Real-time process modeling during discussions is simplified by study S14, 'Live Process Modeling with the BPMN Sketch Miner' [25]. Through the development of a tool using NLP, study S14 enables users to generate and refine BPMN diagrams interactively, fostering collaboration and efficiency in process design. Efforts to automate the generation process persist in study S15, 'Process Model Generation from Natural Language Text' [26]. By employing syntactic and semantic analysis, this methodology converts textual descriptions into process models, streamlining the modeling process and improving efficiency. Proposing a conceptual solution, study S16, 'A Concept for Generating process models from Natural Language Description' [27], bridges the gap between textual descriptions and process models. By utilizing intermediate representations, this approach facilitates the conversion process, enhancing understanding and accuracy. Innovation continues in study S17, 'A Machine Translation Like Approach to Generate process model from Textual Description' [28], where a machine translation-like approach is employed. By leveraging semantic transfer and syntactic manipulations, this study automates the conversion process, simplifying the transition from text to process models. Commonalities between the selected studies. NLP techniques. Studies like 'Extracting process models using NLP Technique' (S1), 'A Proposed Technique for process modeling Diagram using NLP' (S2), and 'Generating BPMN Diagram from Textual Requirements' (S8) employ NLP to analyze and process text for creating process models. Focus on automation.