TaKG: A New Dataset for Paragraph-level Table-to-Text Generation Enhanced with Knowledge Graphs
Year: 2022
Type: conference-paper
Abstract: Table -to-text generation refers to a task that generates text using information provided by a given fact table.We introduce TaKG, a new table-to-text generation dataset with the following highlights: (1) TaKG defines a long-text (paragraph-level) generation task as opposed to well-established short-text (sentence-level) generation datasets.(2) TaKG is the first largescale dataset for this task, containing three application domains and ∼750,000 samples.(3) To address the divergence phenomenon, TaKG enhances table input using external knowledge graphs, extracted by a new Wikidata-based method.We then propose a new Transformerbased multimodal sequence-to-sequence architecture for TaKG that integrates two pretrained language models RoBERTa and GPT-2.Our model shows reliable performance on long-text generation across a variety of metrics, and outperforms existing models for short-text generation tasks.
Authors: Qianqian Qi, Zhenyun Deng, Yonghua Zhu, Lia Jisoo Lee, Michael Witbrock, Jiamou Liu
Institution: University of Auckland
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
OpenAlex ID: W4404784201