Continuous improvement

As explained in Natural Language Processing in Action, Second Edition

A cyclic processing pattern in which system outputs are fed back as inputs for further analysis.

A chatbot can send generated replies back through the same NLP algorithms used to process user statements. The resulting scores or features can feed an objective function that evaluates and selects a reply according to the dialog plan or goals. section 1.8

Figure 1.7 Chatbot recirculating (recurrent) pipeline

The recirculating pipeline shows where generated replies re-enter processing. section 1.8

How Natural Language Processing in Action, Second Edition covers it

How deep it goes

Traces reprocessing and objective-function scoring, without describing updates to model parameters.

Examples

Uses a chatbot pipeline in which generated replies re-enter the algorithms used for user statements.

What you can do after

Evaluate and select chatbot replies by combining response scores or features in an objective function aligned with dialog goals.

How it connects

Connects chatbot pipelines with natural language processing, feature scoring, objective functions, and dialog planning.

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Related concepts

• AI agentused by · An AI agent is a computational system that uses artificial intelligence to interpret information, select actions, and pursue goals through interaction with an environment.
• refactoringcontrasts with · Refactoring is the restructuring of existing software or code to change its internal organization and improve its design, typically while preserving externally observable behavior.
• agile software developmentused by · Agile software development is an iterative approach that delivers software incrementally through collaboration, feedback, adaptive planning, and responsiveness to change.

This explainer was generated by Manning's knowledge graph from the text of Natural Language Processing in Action, Second Edition. The section links point to the parts of the book it draws on.