Chatbots as assistants: an architectural framework
Bibliographic record
Abstract
Automated text-based or speech-based personal assistants, also known as chatbots, have been prevalent in several domains including marketing and technical support. Through mainstream applications, such as Siri or Alexa, their popularity has increased and we now see them being used in even more domains. Although the purpose of chatbots varies among domains, there are common elements that all chatbots share. By identifying these elements, it is possible to streamline the development of chatbots en masse and in a structured manner. Additionally, there can be common challenges in the development of such applications, for example, how to treat novice versus expert users or how to establish memory of the conversation. In this work, we propose a reference architecture for chatbots using concepts from Software Product Lines and Feature Models, where we outline the common elements as well as the common challenges. Using Watson and Bluemix as the basic platforms, we also present the creation of two chatbots, for different purposes, based on this reference architecture to highlight these commonalities.
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".