A Survey of Knowledge-Enhanced Text Generation (2010.04389v4)

Abstract: The goal of text generation is to make machines express in human language. It is one of the most important yet challenging tasks in NLP. Since 2014, various neural encoder-decoder models pioneered by Seq2Seq have been proposed to achieve the goal by learning to map input text to output text. However, the input text alone often provides limited knowledge to generate the desired output, so the performance of text generation is still far from satisfaction in many real-world scenarios. To address this issue, researchers have considered incorporating various forms of knowledge beyond the input text into the generation models. This research direction is known as knowledge-enhanced text generation. In this survey, we present a comprehensive review of the research on knowledge enhanced text generation over the past five years. The main content includes two parts: (i) general methods and architectures for integrating knowledge into text generation; (ii) specific techniques and applications according to different forms of knowledge data. This survey can have broad audiences, researchers and practitioners, in academia and industry.

Knowledge-Enhanced Text Generation: Survey and Future Directions

In the domain of NLP, text generation stands out as a crucial task with applications spanning from machine translation to dialogue systems. Despite significant strides made in neural encoder-decoder models powered by deep learning, the generated text often falls short in richness and informativeness, particularly in real-world settings. The survey paper titled "A Survey of Knowledge-Enhanced Text Generation" addresses this persistent challenge by focusing on the incorporation of knowledge, both internal and external, to enhance text generation systems.

Overview of Knowledge-Enhanced Text Generation

The paper identifies two primary knowledge sources that can be harnessed to enrich text generation: internal knowledge inherent in the input text and external knowledge derived from resources like knowledge bases (KB) and knowledge graphs (KG). The field, referred to as knowledge-enhanced text generation, is systematically examined over several years, highlighting how research has evolved to incorporate various forms of knowledge to enhance LLMs.

The survey categorizes existing methods into general architectures that integrate knowledge into neural networks, including the use of attention mechanisms, memory networks, and the incorporation of graph neural networks (GNNs). By focusing on these architectural advancements, the paper underscores the critical role that structured and unstructured knowledge plays in generating coherent, contextually appropriate text.

Numerical Results and Claims

The survey documents several empirical studies where knowledge-enhanced methods demonstrate improved performance over baseline models. For instance, a knowledge graph-enhanced summarization model showed a substantial increase in Rouge-L scores, indicating more accurate content abstraction and summarization. Moreover, incorporating topic models and keywords in dialogue systems significantly reduced trivial and uninformative responses, thus enhancing user interaction.

Practical and Theoretical Implications

Practically, the survey sheds light on how integrated knowledge can make text generation models more robust and versatile across various domains. Theoretically, it challenges the community to explore the interplay between static knowledge structures like KGs and the dynamic nature of LLMs, prompting research into more adaptive learning techniques.

The paper also explores the challenges faced in the field, such as the difficulty of retrieving relevant knowledge and the potential introduction of noise from irrelevant data sources. Furthermore, the distinctions between internal and external knowledge incorporation reveal varying levels of difficulty and success, where topic models and KGs provide context and factuality while facing interpretability and retrieval challenges.

Future Directions

The authors propose several promising directions for future research:

1. Incorporating Knowledge into Visual-Language Tasks: Expanding beyond text, leveraging visual data to improve multimodal generation tasks is an under-explored area with significant potential.
2. Learning from Broader Sources: Beyond KGs and KBs, exploring other data sources like dictionaries and network structures can enrich text generation with diversified knowledge inputs.
3. Knowledge from Limited Resources: Addressing the challenge of generating knowledge-rich text in low-resource settings can promote equity in AI applications across languages and domains.
4. Continuous Knowledge Learning: Implementing frameworks that adaptively update their knowledge base in real-time can ensure models remain relevant and accurate.

Conclusion

The survey encapsulates a pivotal transformation in NLP—shifting from purely data-driven models to ones that can leverage structured and implicit knowledge. As text generation systems become more nuanced, the integration of varied knowledge forms will be indispensable in tackling the challenges of creating truly intelligent, context-aware AI applications. Rather than being a terminus, this survey frames knowledge-enhanced generation as an evolving research trajectory critical to pushing the boundaries of what AI can achieve in understanding and generating human language.