Knowledge-Enriched Natural Language Generation (2021)

Abstract: Knowledge-enriched text generation poses unique challenges in modeling and learning, driving active research in several core directions, ranging from integrated modeling of neural representations and symbolic information in the sequential/hierarchical/graphical structures, learning without direct supervisions due to the cost of structured annotation, efficient optimization and inference with massive and global constraints, to language grounding on multiple modalities, and generative reasoning with implicit commonsense knowledge and background knowledge. In this tutorial we will present a roadmap to line up the state-of-the-art methods to tackle these challenges on this cutting-edge problem. We will dive deep into various technical components: how to represent knowledge, how to feed knowledge into a generation model, how to evaluate generation results, and what are the remaining challenges?

## introduction natural language generation (nlg) aims at deliberately constructing a natural language text in order to meet specified communicative goals. nlg has been applied in many real-world applications, including dialogue systems, biography generation, technical paper draft generation, and multimedia news summarization. neural language models have achieved impressive successes at automatic nlg, especially on creative writing such as story completion and poetry generation. however, in many downstream applications such as news summarization, counter-argument narrative generation, and knowledge base description, the generation process needs to be guided by certain level of knowledge such as sentiment @xcite , topic @xcite , and style @xcite . the usage of supportive knowledge in nlg can be roughly divided into the following two levels: (1) knowledge description (kd), which transforms structured data into unstructured text, such as topic-to-text @xcite , knowledge base description @xcite @xcite , table-to-text generation @xcite @xcite and its variants in low-resource @xcite and multi-lingual setting @xcite , datato-text @xcite , and graph-to-text @xcite @xcite ; (2) knowledge synthesis (ks), which obtain knowledge from external knowledge resources (e.g, knowledge base) and integrate it into text generation, such as image or video caption generation @xcite , knowledge graph-supported dialogue generation @xcite , knowledge-guided comment generation @xcite , and scientific paper generation @xcite . knowledge-enriched text generation poses unique challenges in modeling and learning, driving active research in several core directions, ranging from integrated modeling of neural representations and symbolic information in the sequential/hierarchical/graphical structures, learning without direct supervisions due to the cost of structured annotation, efficient optimization and inference with massive and global constraints, to language grounding on multiple modalities, and generative reasoning with implicit commonsense knowledge and background knowledge. in this tutorial we will present a roadmap to line up the state-of-the-art methods to tackle these challenges on this cuttingedge problem. we will dive deep into various technical components (as shown in figure 1 ): how to represent knowledge, how to feed knowledge into a generation model, how to evaluate generation results, and what are the remaining challenges? ## diversity considerations the topic to be presented is of great interest to diverse group of audience from academics and industry. we will cover a broad diversity of methods and applications in many languages and domains. in particular, enriching modeling and learning with external knowledge, as the core topic in this tutorial, is particularly helpful for low-resource language modeling where no large data are available. we have a diverse instructor team across multiple institutions (nd, uiuc, ucsd, and salesforce inc.) with varying seniority (ranging from junior/senior phd students to assistant/full professors and senior researchers), two of whom are female researchers. the team has a diverse and broad expertise in natural language processing and generation, machine learning, data mining, and various application domains. ## prerequisites this tutorial will present basic and advanced methods in nlg systematically to audience. the audi-ence may find different useful content when have different levels of prior knowledge: (with the number of ⭐ for how much a person may feel comfortable and confident with the subject matter) ## reading list full reading list: