Exercise: Poem Generation and Analysis

Task

Text generation is an NLP task to generate a sequence of words that closely relate to the context and pattern observed in the prompt. A specific use case of generative AI is poetry generation. In technical terms, poem generation is a creative and challenging task to produce coherent, meaningful, and well-structured text for the rhythmic style and structure of poetry. The generation process uses a blend of creativity and generative prowess to generate poems that better fit the context. Poem analysis is another text-generative NLP task that analyzes the given poem based on certain metrics or features, such as theme, rhythmic styles, syllable pattern, and paraphrasing. It helps to understand the linguistic and structural patterns of the poem being analyzed.

Expected outputs

The output of the text generation task varies for each execution of the generative NLP function. However, the context of the generated text matches the context provided in the prompt. For poem generation, the metrics for scenario, structure, and theme are met. In the process, criteria for scenario, structure, and theme are considered by the algorithm to help generate concise and focused poems. For the analysis of the poem, the metrics for evaluation of the poem are usually defined in the given prompt. When evaluating the poem, the algorithm analyzes the poem and assigns values to the requested metrics outlined in the prompt.

Applications of generation and analysis

Text generation and analysis have many real-world use cases that help in efficient product development, code analysis, marketing, and research. Some of the applications are:

• Content creation in marketing: Text generation is valuable in producing content at a large scale. Text generation can help produce engaging content to attract customers and can help market the products.

• Language translation: Text generation can help translate text from one language to another.

• Code generation: Automated code generation can simplify software development. It enhances code efficiency and reduces the risk of errors.

• News analysis: The text summarization and analysis algorithm processes a large number of news articles and generates precise and efficient summaries.

Examples

In the example below, we give the prompt “a lonely pigeon in the dark” to generate a poem. We also include metrics for both theme and structure. The generated poem, as well as its analysis, are included in the slideshow.