Text generation, also referred to as natural language generation, has emerged as a pivotal subfield within natural language processing (NLP). Its objective is to craft coherent and comprehensible text in human language from diverse input sources such as text, images, tables, and knowledge bases.
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Over the past decades, text-generation techniques have been widely deployed across various applications. These encompass generating responses in dialog systems during conversations, translating text between languages, and producing concise summaries of source texts. The primary ambition of text generation is to autonomously learn the mapping from input data to output, establishing end-to-end solutions with minimal human intervention. This mapping empowers the generation system to extrapolate across diverse contexts, generating free-flowing text under specified conditions.
Models for Text Generation
Historically, statistical language models using n-grams or Markov chains were employed to predict word probabilities in given contexts. While effective to a degree, these methods struggled with context complexity. However, the advent of deep learning, particularly neural network models, revolutionized text generation. These models, notably those employing sequence-to-sequence frameworks such as the encoder-decoder architecture, utilize embeddings to enhance their ability to comprehend input-output relationships. Techniques such as attention and copy mechanisms further augment text generation quality.
Crucially, deep neural networks enable holistic learning of semantic mappings without labor-intensive feature engineering. They also employ low-dimensional semantic representations, mitigating data sparsity challenges. Yet, significant challenges persist. The availability of sizable labeled datasets remains a performance bottleneck, restricting applications in domains lacking annotated examples. Despite strides in deep neural models, they can struggle with intricate context-word relationships.
Deep Generative Models for Text Generation
Deep generative models play a crucial role in both assessing the model's learning performance and understanding problem domains. In the domain of text generation within the deep learning era, two prominent techniques are variational auto-encoders (VAEs) and generative adversarial networks (GANs).
Deep learning models heavily rely on well-labeled data, often posing challenges when confronted with unstructured or unlabeled data. VAEs, a robust deep generative model, offer a solution for training on unlabeled data. VAEs consist of an encoder that maps data to latent variables and a decoder that reconstructs data from these latent variables. This process involves encoding data into a latent space using an encoder, represented by parameters, and decoding from this space using a decoder with parameters. The model's objective function comprises a reconstruction loss and a regularization term. This regularization term ensures proximity between the latent distribution and a prior distribution, which in turn facilitates the generation of new data.
GANs have introduced a game-changing approach to deep generative models. GANs consist of two models: a discriminator and a generator. The generator generates data samples, while the discriminator distinguishes real data from generated data. The training process revolves around a minimax game where the generator aims to create samples resembling real data, while the discriminator seeks to correctly classify these samples.
GANs have demonstrated remarkable success in image generation but applying them to text generation is more complex due to the discrete nature of text. Although attempts such as Professor Forcing and scheduled sampling have been made to address challenges such as exposure bias, GANs for text generation are still an active research area.
Text Generation using Pre-trained Language Models
A text is represented as a sequence of tokens drawn from a vocabulary, with text generation aiming to produce coherent and fluent language. Text generation involves generating human-readable text based on input data, often conditioned on desired language properties. The text generation process is formalized as a function involving a pre-trained language model (PLM).
Text generation tasks are classified based on input data types and desired properties:
- Language modeling, or unconditional text generation, arises when input data is absent, focusing on fluency and naturalness.
- Topic-to-text and attribute-based generation utilize discrete attributes to control content.
- Data-to-text generation involves structured data such as tables or knowledge bases, aiming for objective and accurate descriptions.
- Image captioning and speech recognition adapt PLMs for multimedia input.
Recent years have witnessed the ascension of PLMs in NLP. This entails pre-training models on extensive unsupervised data and fine-tuning them for specific tasks. The PLMs, built on the Transformer architecture, encode linguistic knowledge into their parameters. Notably, PLMs such as bidirectional encoder representations from transformers (BERT) and generative pre-trained transformers (GPT) have demonstrated the encoding of substantial linguistic knowledge and contextual representations.
Applying PLMs to text generation, grounded in their accurate language understanding and fluency, is a promising direction both in academia and industry. PLMs also extend to other tasks. Three key aspects of applying PLMs to text generation are identified: encoding input data, designing adaptable PLM architectures, and developing optimization algorithms to satisfy desired text properties. Addressing these aspects is vital for effectively leveraging PLMs for text-generation tasks.
Applications of Text Generation
The phenomenon of text generation encompasses diverse applications. The key applications are machine translation, text summarization, and dialogue systems.
Machine Translation: Machine translation, a process of automated language conversion, has been notably revolutionized by Neural Machine Translation (NMT) through deep learning. This is classified into unsupervised and supervised types, depending on the availability of parallel corpora for PLM fine-tuning. In unsupervised machine translation (UMT), PLMs are trained using monolingual corpora without parallel data.
This method eschews reliance on vast annotated data, significantly benefiting low-resource language translation. The process generally involves pre-training PLMs on diverse monolingual data, followed by iterative back-translation combining source-to-target and target-to-source models. In supervised machine translation (SMT), PLMs are fine-tuned with parallel corpora. This involves directly fine-tuning unsupervised PLMs using bilingual pairs or designing PLMs for parallel corpora by pre-training bilingual pairs with supervised Seq2Seq loss.
Text Summarization: Text summarization entails condensing textual content into concise summaries through extraction or abstraction. In document summarization, PLMs such as the text-to-text transfer transformer (T5), the abstract text summarization model BART, and pre-training with extracted gap sentences for abstractive summarization (PEGASUS) can be directly fine-tuned. Some studies integrate external guidance, extract keywords or sentences, and use topic models to enhance generation quality.
For dialogue summarization, where multi-turn conversations are summarized, methods include the direct application of document summarization models. Some studies segment dialogues into chunks, generate partial summaries, and then compile complete summaries.
Dialogue Systems: Dialogue systems, also known as conversational agents, engage in fluid human-machine interactions. These are divided into open-domain and task-oriented systems, addressing general or specific topics. Open-domain systems such as chatbots rely on continual pre-training on informal text resources. Some models incorporate human interaction, such as responses through back-translation, mutual information maximization, and unlikelihood training. Task-oriented systems involve natural language understanding, dialogue state tracking, policy learning, and natural language generation. Models such as TransferTransfo and hierarchical encoders cater to these tasks, while style enforcement and controllability are also considered.
Challenges and Future Directions
Despite significant advancements, several open problems persist, offering promising avenues for future exploration.
Controllable Generation: Steering text attributes such as sentiment and coherence remain challenging. While control codes have been investigated, fine-grained and multi-attribute control necessitates further research to create more flexible and versatile PLMs.
Optimization Exploration: Current optimization methods largely rely on fine-tuning, with prompt-based learning showing promise. Exploring a broader range of optimization approaches to combine advantages from existing methods holds potential.
Language-agnostic PLMs: Existing PLMs predominantly cater to English, presenting hurdles for non-English tasks. Developing language-agnostic PLMs requires capturing universal linguistic features, possibly through repurposing English-based models for non-English languages.
Ethical Concerns: PLMs, trained on large-scale, unfiltered corpora, can lead to ethical dilemmas such as generating private content and biased output. Researchers must address these concerns by implementing safeguards against misuse and prejudice.
The ability of artificial intelligence (AI) to generate text is a tribute to its amazing powers. Its transformational impact extends across industries, providing efficiency, convenience, and new creative opportunities. However, its development must be guided by ethical considerations to ensure the happy coexistence of human creativity with AI invention. The text creation journey is still underway, and its destination is fascinating and deeply significant.
References and Further Readings
Touseef Iqbal, Shaima Qureshi. (2022). The survey: Text generation models in deep learning, Journal of King Saud University - Computer and Information Sciences, 34, Issue 6, Part A, 2515-2528. DOI: https://doi.org/10.1016/j.jksuci.2020.04.001
Junyi Li and Tianyi Tang and Wayne Xin Zhao and Ji-rong Wen. (2021). Pretrained Language Models for Text Generation: A Survey. arXiv. DOI: https://doi.org/10.48550/arXiv.2201.05273
N. Fatima, A. S. Imran, Z. Kastrati, S. M. Daudpota and A. Soomro. (2022). A Systematic Literature Review on Text Generation Using Deep Neural Network Models, IEEE Access, 10, 53490-53503. DOI: https://doi.org/10.1109/ACCESS.2022.3174108