A DSL-Based Approach for Developing Dialogflow Chatbots: Modeling and Code Generation Templates
Bibliographic record
Abstract
In recent years, chatbots have marked significant advancements within the field of Artificial Intelligence (AI). These systems are extensively utilized to offer users rapid and continuous access to services through natural language interfaces. However, the heterogeneity and diversity of chatbot development tools, combined with their reliance on natural language processing (NLP) services, introduce substantial complexity in their design. To address these challenges, this research proposes the development of a domain-specific language (DSL) aimed at accelerating chatbot development. A DSL is a specialized programming language that provides expressive power tailored to a specific domain through appropriate abstraction notations. It comprises three essential components: abstract syntax, concrete syntax, and semantics. This study focuses on designing a metamodel that encapsulates all the concepts of the Dialogflow platform. Additionally, it provides an overview of Model-Driven Engineering (MDE) and suggests model transformations to automatically generate the source code of a chatbot conforming to the specific implementation platform, Dialogflow. This approach introduces the necessary assets to facilitate the automatic generation of code, thereby streamlining the development process.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".