A Sketch of DSL and Code Generator for Accelerating Chatbot Development
Bibliographic record
Abstract
In today’s world, chatbots have become a significant advancement in Artificial Intelligence (AI). They are extensively utilized to provide users convenient access to 24/7 services using natural language. The development of these conversational applications is evolving rapidly and necessitating specific knowledge and practical experience to successfully exploit all the functionalities of chatbot development platforms and frameworks. The heterogeneity of chatbot development tools and their need for NLP services makes it challenging to build chatbots. Thus, one possible solution to these problems is to construct a domain-specific language (DSL) to accelerate the development of Chatbots. A Domain Specific Language (DSL) is a programming language that provides expressive power within a specific problem domain by using appropriate abstraction notations. Abstract syntax, concrete syntax, and semantics are the three components that describe it. Furthermore, it is necessary to utilize generation templates to construct a chatbot for an already established platform. Through the use of a Model-Driven Architecture (MDA), which is an approach that focuses on modeling software systems at different levels of abstraction, from high-level requirements to platform-independent designs, this work aims to define a sketch of an independent language of the chatbot development platform by providing the components needed for our DSL, like metamodel for modeling conversations and developing transformations between models to generate the source code for a chatbot conforming to a specific implementation platform. This will facilitate the automatic generation of code.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.014 | 0.009 |
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".