Human evaluation in natural language generation (NLG) has become a hot topic lately, with the emergence of several survey papers on the topic that study how human evaluation has been conducted in the past in the field of NLG in general.

Not all natural language generation tasks are of the nature that they are designed to convey factual information. Some of the NLG tasks deal with producing text of aesthetic nature such as poetry, stories, humor and so on. We call these creative NLG tasks. 

These types of tasks are simultaneously researched in two distinct fields of science: natural language processing (NLP) and computational creativity (CC).

Human evaluation in natural language generation (NLG)

Hämäläinen, M., & Alnajjar, K. (2021). Human Evaluation of Creative NLG Systems: An Interdisciplinary Survey on Recent Papers. arXiv preprint arXiv:2108.00308. https://arxiv.org/abs/2108.00308

University of Michigan - Ann Arbor

Google

NLG, a subfield of artificial intelligence (AI), is a software process that automatically transforms data into plain-English content.

Natural language generation

Autocorrection is an automatic text validation tool commonly found in your phone's and computer's text editing interfaces. This tool can correct spelling errors and/or contextual errors depending on the algorithms.

Autocorrection

The technique (Shannon, 1951) generates a word in a sequence given the previous words, using cross-entropy as the loss function and perplexity as the evaluation method. The sequence ends once it reaches a predefined length. Each softmax layer is to decide the next word based on the current hidden state in the RNN component. The initial input token(s) would depend on the NLP task's nature (e.g. machine translation, question answering, summarization). 

RNN's Approach to Text Generation in NLP (Autoregressive Generation)

inform code-switching in multilingual contexts

A Survey of Code-switching: Linguistic and Social Perspectives for Language Technologie

Human Evaluation of Creative NLG Systems: An Interdisciplinary Survey on Recent Papers

- To present a comprehensive overview of NLG developments since 2000.
- To provide researchers with synthesis and pointers to relevant research.
- To introduce the field to researchers who are less familiar.

Goals of the NLG survey

- Content determination:  Deciding which information to include in the text under construction.
- Text structuring: Determining in which order information will be presented in the text.
- Sentence aggregation: Deciding which information to present in individual sentences.
- Lexicalisation:  Finding the right words and phrases to express information.
- Referring expression generation:  Selecting the words and phrases to identify domain objects.

NLG tasks.

GRL Book

A survey of embedding models of entities and relationships for knowledge graph completion

In Natural Language Processing, text generation tasks that use existing text as input can be broadly divided into two main categories. The first is text completion, which involves continuing to write based on the provided text. The second is text transformation, which focuses on converting the input text into a new, different text.

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