Methods for Text Generation: From Markov Chains to RNNs

The automatic generation of natural language text is an active area of research that has recently gained more mainstream interest due to the successes of large language models.

The ability to produce coherent, creative, and grammatically correct text without human intervention opens up many new opportunities for various industries and applications. Multiple techniques have been developed over the years to tackle the challenging problem of text generation, the most prominent of which currently are Markov chains, recurrent neural networks, and transformer architectures.

Techniques for Text Generation

Markov Chains

Markov chains are a simple statistical model that generates text by predicting the next token (word or character) based only on the previous sequence of tokens. They do not have any notion of long-term dependencies or memory, making them a straightforward method to implement but one that typically produces repetitive and incoherent text. The lack of a mechanism to represent more complex, abstract language concepts and long-term context limits their effectiveness for most creative generation tasks. However, they can function reasonably well for simple objectives such as grammar correction where only short-range context is required.

Recurrent Neural Networks (RNNs)

Recurrent neural networks (RNNs) are a class of neural networks designed to handle sequential data by maintaining an internal state. This makes them well-suited for natural language processing and generation tasks.  RNNs are able to generate more coherent and creative text than Markov chains by learning complex patterns in large datasets. However, standard RNN architectures struggle with retaining long-term dependencies due to issues like vanishing and exploding gradients. Variations like long short-term memory (LSTM) networks and gated recurrent units (GRUs) have been developed to address these weaknesses. While RNNs have achieved some success in generation tasks like story and poem generation, they require large datasets and computational resources to train.

Transformers

Transformers are a type of neural network architecture that relies entirely on an attention mechanism to draw global dependencies between input and output. They do not have any notion of sequence, processing the entire input sequence at once. As a result, transformers are able to effectively represent long-term context and semantics, overcoming one of the core weaknesses of RNNs. Since their introduction, transformer models have achieved state-of-the-art results in many natural language processing tasks, including text generation. Examples of transformer-based generative models include GPT-3 and GPT-4, which can generate long-form, coherent articles and stories. However, like RNNs, transformers also require a large amount of data and computing power to train.

Integrating these Techniques

Text generation techniques have a wide range of promising applications across industries. For creative works, they can be used to generate stories, scripts, poems, song lyrics, and more. With a large corpus of existing works, an RNN or transformer model could be trained to produce new pieces in a similar style by learning patterns in rhythm, rhyme, theme, plot, and other aspects of the creative work.

For marketing and advertising, text generation is useful for automating the production of product descriptions, blog posts, social media content, and other promotional copy. An enterprise could feed a model its product catalog, brand guidelines, and a dataset of human-written marketing content. The model would then be able to generate new copy in the appropriate brand voice and with the relevant product specifications and features. This could significantly reduce the time and costs associated with human content creation.

In customer support, text generation techniques enable the automatic generation of FAQs, troubleshooting guides, product reviews and more. A model could be trained on a company's product documentation, support tickets, and customer feedback to produce relevant, helpful content. For example, a text generation model could be trained on a dataset of customer support tickets. This model could then be used to generate FAQs that are tailored to the specific needs of different customers. This benefits both the customers in enabling quick self-service as well as the company in reducing support costs.

For education, text generation can be used to generate lesson plans, quizzes, examples, and other instructional content. A model trained on course curricula and educational datasets could help instructors design lesson plans and assignments. Text generation also shows promise for providing personalized learning by generating exercises tailored to a student's needs and progress.

Conclusion

In sum, text generation techniques offer a myriad of opportunities to improve, automate and personalize content creation across domains. However, the chosen technique - whether Markov chains, RNNs or transformers - depends strongly on the level of quality, creativity and coherence needed for the particular application. Simpler objectives may be sufficiently met with Markov chains or RNNs while more complex, long-form generation tasks will benefit strongly from the global context that transformer architectures provide.

With increasingly large datasets and computational resources, text generation tools will continue to become more capable and ubiquitous. Rather than replacing human writers and creators, they are more likely to augment human capabilities by taking over repetitive and mundane content creation tasks. The future of text generation is an exciting one with many new possibilities for businesses and industries on the horizon.

Get in touch

If you are interested in learning more about how text generation can be used in your business, I encourage you to sign up and explore the Lettria NLP platform. We would be happy to show you how our platform can help you automate your content creation process and generate high-quality, creative text.

To learn more, explore our other blog articles like The Importance of Disambiguation in Natural Language Processing and The Future of AI Text Analysis: Graph-Based vs. Conversational AI or contact one of our NLP experts today.