Technical perspective of TURL: Table understanding through representation learning

Several efforts aim at representing tabular data with neural models for supporting target applications at the intersection of natural language processing (NLP) and databases (DB) [1-3]. The goal is to extend to structured data the recent neural architectures, which achieve state of the art results in NLP applications. Language models (LMs) are usually pre-trained with unsupervised tasks on a large text corpus. The output LM is then fine-tuned on a variety of downstream tasks with a small set of specific examples. This process has many advantages, because the LM contains information about textual structure and content, which are used by the target application without manually defining features.