What is text generation?
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Published: 19 March 2024
Contributor: Vrunda Gadesha, Eda Kavlakoglu

Text generation is the process of automatically producing coherent and meaningful text, which can be in the form of sentences, paragraphs, or even entire documents. It involves various techniques, which can be found under the field such as natural language processing (NLP), machine learning, and deep learning algorithms, to analyze input data and generate human-like text. The goal is to create text that is not only grammatically correct but also contextually appropriate and engaging for the intended audience.

 

The history of text generation can be traced back to early computer science research in the 1950s and 1960s. However, the field truly took off in the 1980s and 1990s with the advent of artificial intelligence and the rise of machine learning algorithms. In recent years, advancements in deep learning and neural networks have led to significant improvements in the quality and diversity of generated text.1

Difference between natural language understanding (NLU) and natural nanguage generation (NLG)

Natural Language Generation (NLG) and Natural Language Understanding (NLU) are two essential components of a robust natural language processing (NLP) system, but they serve different purposes.

Natural Language Understanding (NLU) Is the ability of a machine to comprehend, interpret, and extract meaningful information from human language in a valuable way. It involves tasks like sentiment analysis, named entity recognition, part-of-speech tagging, and parsing. NLU helps machines understand the context, intent, and semantic meaning of human language inputs.

Natural Language Generation (NLG) Is the ability of a machine to produce human-like text or speech that is clear, concise, and engaging. It involves tasks like text summarization, storytelling, dialogue systems, and speech synthesis. NLG helps machines generate meaningful and coherent responses in a way that is easily understood by humans.

NLU focuses on understanding human language, while NLG focuses on generating human-like language. Both are crucial for building advanced NLP applications that can effectively communicate with humans in a natural and meaningful way.

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Benefits of text generation
  • Improved Efficiency: Text generation can significantly reduce the time and effort required to produce large volumes of text. For instance, it can be used to automate the creation of product descriptions, social media posts, or technical documentation. This not only saves time but also allows teams to focus on more strategic tasks.2

  • Enhanced Creativity: Artificial intelligence can generate unique and original content with high speed that may not be possible for humans to produce manually. This can lead to more innovative and engaging content, such as stories, poems, or music notes. Additionally, text generation can help overcome writer's block by providing new ideas and perspectives.

  • Increased Accessibility: Text generation can assist individuals with disabilities or language barriers by generating text in alternative formats or languages. This can help make information more accessible to a wider range of people, including those who are deaf or hard of hearing, non-native speakers, or visually impaired.

  • Better Customer Engagement: Personalized and customized text generation can help businesses and organizations better engage with their customers. By tailoring content to individual preferences and behaviors, companies can create more meaningful and relevant interactions, leading to increased customer satisfaction and loyalty.

  • Enhanced Language Learning: Text generation can be a useful tool for language learners by providing feedback and suggestions for improvement. By generating text in a specific language style or genre, learners can practice and develop their writing skills in a more structured and guided way.

Learn more about IBM Watson Natural Language Understanding
Challenges of text generation techniques

In the text generation techniques, several challenges arise that need to be addressed for these methods to reach their full potential. These challenges include ensuring the quality of generated text, promoting diversity in the generated output, and addressing ethical considerations as well as privacy concerns.

  • Quality: One of the most significant challenges in text generation is ensuring the quality of the generated text. The generated text should be coherent, meaningful, and contextually appropriate. It should also accurately reflect the intended meaning and avoid generating misleading or incorrect information.

  • Diversity: A second challenge in text generation is promoting diversity in the generated output. While it is important for the generated text to be accurate and consistent, it is also crucial that it reflects a wide range of perspectives, styles, and voices. This challenge is particularly relevant in applications such as natural language processing, where the goal is to create text that is not only accurate but also engaging and readable.

  • Ethics and Privacy: A third challenge in text generation is addressing ethical considerations and privacy concerns. As text generation techniques become more sophisticated, there is a risk that they could be used to generate misleading or harmful text, or to invade people's privacy.

The challenges of text generation techniques are significant and require careful consideration and attention. These challenges are addressed with advance techniques like statistical models, Neural networks, and transformer-based models. These models can be adopted with APIs, open-source python scripts. Fine tuning these models will provide high quality, diverse, logically correct and ethically sound text.  Along with this it is essential to ensure that text generation techniques along with generative AI are used responsibly and effectively, and for maximizing their benefits and minimizing their risks.3

Learn more about IBM Watson Discovery
Text generation techniques
  • Statistical Models: These models typically use a large dataset of text to learn the patterns and structures of human language, and then use this knowledge to generate new text. Statistical models can be effective at generating text that is similar to the training data, but they can struggle to generate text that is both creative and diverse. N-gram Models and Conditional Random Fields (CRF) are popular statistical models.

    • N-gram Models: These are a type of statistical model that use the n-gram language model, which predicts the probability of a sequence of ‘n-items’ in a given context.10

    • Conditional Random Fields (CRFs): These are a type of statistical model that use a probabilistic graphical model to model the dependencies between words in a sentence. CRFs can be effective at generating text that is both coherent and contextually appropriate, but this type of text generation model can be computationally expensive to train and may not perform well on tasks that require a high degree of creative language generation.11

  • Neural Networks: These are machine learning algorithms utilizing artificial neural networks to identify data patterns. Via APIs, developers can tap into pre-trained models for creative and diverse text generation, closely mirroring the training data's complexity. The quality of the generated text heavily relies on the training data. However, these networks demand significant computational resources and extensive data for optimal performance.4

    • Recurrent Neural Networks (RNNs): These are a foundational type of neural network optimized for processing sequential data, such as word sequences in sentences or paragraphs. They excel in tasks that require understanding sequences, making them useful in the early stages of developing large language models(LLMs). However, RNNs face challenges with long-term dependencies across extended texts, a limitation stemming from their sequential processing nature. As information progresses through the network, early input influence diminishes, leading to the "vanishing gradient" problem during backpropagation, where updates shrink and hinder the model's ability to maintain long-sequence connections. Incorporating techniques from reinforcement learning can offer strategies to mitigate these issues, providing alternative learning paradigms to strengthen sequence memory and decision-making processes in these networks.5

    • Long Short-Term Memory Networks (LSTMs): This is a type of neural network that use a memory cell to store and access information over long periods of time. LSTMs can be effective at handling long-term dependencies, such as the relationships between sentences in a document, and can generate text that is both coherent and contextually appropriate.6

  • Transformer-based Models: These models are a type of neural network that use self-attention mechanisms to process sequential data. Transformer-based models can be effective at generating text that is both creative and diverse, as they can learn complex patterns and structures in the training data and generate new text that is similar to the training data. Unlike historical approaches like RNNs and LSTMs, transformer-based models have the distinct advantage of processing data in parallel, rather than sequentially. This allows for more efficient handling of long-term dependencies across large datasets, making these models especially powerful for natural language processing applications such as machine translation and text summarization.7

    • Generative Pretrained Transformer (GPT): GPT is a transformer-based model that is trained on a large dataset of text to generate human-like text. GPT can be effective at generating text that is both creative and diverse, as it can learn complex patterns and structures in the training data and generate new text that is similar to the training data.8

    • Bidirectional Encoder Representations from Transformers (BERT): BERT is a transformer-based model that is trained on a large dataset of text to generate bidirectional representations of words. That means it evaluates the context of words from both before and after in a sentence. This comprehensive context awareness allows BERT to achieve a nuanced understanding of language nuances, resulting in highly accurate and coherent text generation. This bidirectional approach is a key distinction that enhances BERT's performance in applications requiring deep language comprehension, such as question answering and named entity recognition (NER), by providing a fuller context compared to unidirectional models.9

Thus, text generation techniques, especially those implemented in Python, have revolutionized the way we approach generative AI in the English language and beyond. Leveraging trained models from platforms like Hugging Face, developers and data scientists can access a plethora of open-source tools and resources that facilitate the creation of sophisticated text generation applications. Python, being at the forefront of AI and data science, offers libraries that simplify interacting with these models, allowing for customization through prefix or template adjustments, and the manipulation of text data for various applications. Furthermore, the use of metrics and benchmarks to evaluate model performance, along with advanced decoding strategies, ensures that the generated text meets high standards of coherence and relevance.

How BERT and GPT models change the game for NLP
Examples of text generation

Text generation is a versatile tool that has a wide range of applications in various domains. Here are some examples of text generation applications:

 

Blog Posts and Articles:

It can be used to automatically generate blog posts and articles for websites and blogs. These systems can automatically generate unique and engaging content that is tailored to the reader's interests and preferences.

News Articles and Reports:

It can be used to automatically generate news articles and reports for newspapers, magazines, and other media outlets. These systems can automatically generate timely and accurate content that is tailored to the reader's interests and preferences.

Social Media Posts:

It can be used to automatically generate social media posts for Facebook, Twitter, and other platforms. These systems can automatically generate engaging and informative content that is tailored to the reader's interests and preferences.

Product Descriptions and Reviews:

It can be used to automatically generate product descriptions and reviews for e-commerce websites and online marketplaces. These systems can automatically generate detailed and accurate content that is tailored to the reader's interests and preferences.

Creative Writing:

It can be used to automatically generate creative writing prompts for writers with powerful AI models. These systems can automatically generate unique and inspiring ideas that are tailored to the writer's interests and preferences.

Language Translation:

It can be used to automatically translate text between different languages. These systems can automatically generate accurate and fluent translations that are tailored to the reader's interests and preferences.

Chatbot Conversations:

It can be used to automatically generate chatbot conversations for customer service and support. These systems can automatically generate personalized and engaging conversations that are tailored to the reader's interests and preferences.

Text summaries

It condenses lengthy documents into concise versions, preserving key information through advanced natural language processing and machine learning algorithms. This technology enables quick comprehension of extensive content, ranging from news articles to academic research, enhancing information accessibility and efficiency.

Virtual Assistant Interactions:

Text generation can be used to automatically generate virtual assistant interactions for home automation and personal assistance. These systems can automatically generate personalized and convenient interactions that are tailored to the reader's interests and preferences.

Storytelling and Narrative Generation:

Text generation can be used to automatically generate stories and narratives for entertainment and educational purposes. These systems can automatically generate unique and engaging stories that are tailored to the reader's interests and preferences.

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Footnotes


Lin, Z., Gong, Y., Shen, Y., Wu, T., Fan, Z., Lin, C., ... & Chen, W. (2023, July). Text generation with diffusion language models: A pre-training approach with continuous paragraph denoise. In International Conference on Machine Learning (pp. 21051-21064). PMLR.

Prabhumoye, S., Black, A., & Salakhutdinov, R. (2020). Exploring Controllable Text Generation Techniques. , 1-14. https://doi.org/10.18653/V1/2020.COLING-MAIN.1.

Yu, W., Yu, W., Zhu, C., Li, Z., Hu, Z., Wang, Q., Ji, H., & Jiang, M. (2020). A Survey of Knowledge-enhanced Text Generation. ACM Computing Surveys, 54, 1 - 38. https://doi.org/10.1145/3512467.

Zhang, Y. (2020). Deep Learning Approaches to Text Production. Computational Linguistics, 46, 899-903. https://doi.org/10.1162/coli_r_00389.

Su, Y., Lan, T., Wang, Y., Yogatama, D., Kong, L., & Collier, N. (2022). A Contrastive Framework for Neural Text Generation. ArXiv, abs/2202.06417.

S. Chandar, M. M. Khapra, H. Larochelle and B. Ravindran, "Correlational Neural Networks," in Neural Computation, vol. 28, no. 2, pp. 257-285, Feb. 2016, doi: 10.1162/NECO_a_00801.

Rahali, A., & Akhloufi, M. A. (2023). End-to-end transformer-based models in textual-based NLP. AI, 4(1), 54-110.

Khalil, F., & Pipa, G. (2021). Transforming the generative pretrained transformer into augmented business text writer. Journal of Big Data, 9, 1-21. https://doi.org/10.1186/s40537-022-00663-7.

Devlin, J., Chang, M., Lee, K., & Toutanova, K. (2019). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. , 4171-4186. https://doi.org/10.18653/v1/N19-1423.

10 M. Suzuki, N. Itoh, T. Nagano, G. Kurata and S. Thomas, "Improvements to N-gram Language Model Using Text Generated from Neural Language Model," ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Brighton, UK, 2019, pp. 7245-7249, doi: 10.1109/ICASSP.2019.8683481.

11 D. Song, W. Liu, T. Zhou, D. Tao and D. A. Meyer, "Efficient robust conditional random fields," in IEEE Transactions on Image Processing, vol. 24, no. 10, pp. 3124-3136, Oct. 2015, doi: 10.1109/TIP.2015.2438553.