Lia Jisoo Lee


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TaKG: A New Dataset for Paragraph-level Table-to-Text Generation Enhanced with Knowledge Graphs
Qianqian Qi | Zhenyun Deng | Yonghua Zhu | Lia Jisoo Lee | Michael Witbrock | Jiamou Liu
Findings of the Association for Computational Linguistics: AACL-IJCNLP 2022

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 large-scale 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 Transformer-based 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.