When an NLG algorithm fails to understand some part of the user’s utterance, instead of using a reject dialogue act (ex: "Please repeat"), it uses targeted clarification questions that reprise elements of the misunderstanding. For example:

Hamilton College

Language Generation is a sub-field of the field of Natural Language Processing (NLP), Artificial Intelligence (AI) and Cognitive Science (CS).

For natural language generation in the dialogue-state model, its task is to generate the text of the response to the user once a dialogue act has been decided. 

Natural Language Generation (NLG)

An on-going but a helpful book resource about NLP
https://web.stanford.edu/~jurafsky/slp3/

Speech and Language Processing (3rd ed. draft) 

The task of natural language generation (NLG) in the information-state architecture is often modeled in two stages, content planning and sentence realization.

There is also a three stage pipeline architecture proposed by Reiter and Dale focusing on the aspects of document planning, sentence planning and surface realization.

NLG Stages in the Information-state Architecture

Clarification Questions

Santhanam, Sashank and Shaikh, Samira. A Survey of Natural Language Generation Techniques with a Focus on Dialogue Systems - Past, Present and Future Directions. arXiv, https://arxiv.org/abs/1906.00500.

A Survey of Natural Language Generation Techniques with a Focus on Dialogue Systems - Past, Present and Future Directions

Reiter and Dale presented a standard architecture for the developing NLG systems that comprised of six components each performing an important task to generate a coherent output:
- Content determination
- Document structuring
- Lexicalization
- Referring expression generation
- Sentence aggregation
- Linguistic realization

NLG Six-Stage Pipeline Architecture

Applying deep neural networks to Natural Language Processing has helped achieve state-of-the-art performance across different tasks, including the task of language generation due to the capability of neural networks to learn representations with different levels of abstraction. There are four main approaches that have been applied to the task of language generation:
- Language models
- Encoder-decoder architecture
- Memory networks
- Transformer models

Deep Learning approaches for Language Generation

Dialogue Systems or Conversational Agents (CA) are designed with the intention of generating meaningful and coherent responses that are easy to respond to and informative when the system is engaged in a conversation with humans. A good dialogue model incorporated in conversational agents should be able to generate dialogues with high similarity to how humans converse

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