Research on Text Generation for Structured Data

The talk focuses on the research of text generation for structured data, a hot scenario in AIGC where inputs are triples, tables, or other non‑linguistic structures, requiring higher generation capabilities than traditional ChatGPT tasks.

It reviews the development stages of artificial intelligence, from early breakthroughs like AlphaGo to recent conversational models such as ChatGPT, GPT‑4 and Wenxin Yiyan, highlighting their ability to understand intent and generate coherent text.

Text generation is introduced as a field encompassing traditional tasks (summarization, translation, paraphrasing) and newer tasks that generate natural language from structured inputs such as tables, knowledge‑graph triples, or multimodal data.

Model evolution is described: early pipeline methods that plan content and fill templates, the shift to end‑to‑end encoder‑decoder architectures in 2014, and the emergence of large pretrained models (GPT‑1/2/3, Megatron‑Turning, OPT) that improve fluency and coherence.

Current challenges are outlined: Content selection : deciding which triples or table cells to verbalize. Numeric representation : embedding size and comparison information to avoid factual errors. Numeric reasoning : inferring values not explicitly present in the table. Style control : generating text that matches a desired writing style or genre. These challenges are addressed with hierarchical row/column encoders, multi‑dimensional rewards, dual decoders with slot‑filling, and the tablemask strategy for masked table reconstruction.

The article also discusses evaluation metrics specific to structured‑data generation, such as content selection (CS), relation generation (RG), and content ordering (CO), alongside classic metrics like BLEU and ROUGE.

A brief Q&A section answers practical questions about triple encoding, large‑scale table modeling, temporal embeddings for time‑series tables, and the future impact of large language models on knowledge‑graph research.

In summary, the work emphasizes the growing importance of structured‑data text generation, the need for specialized modeling techniques, and open research opportunities in numeric reasoning, multimodal integration, and style‑aware generation.