STable: Table Generation Framework for Encoder-Decoder Models
This addresses the need for robust table generation in NLP for applications such as knowledge base population, though it is an incremental improvement over existing sequential methods.
The paper tackles the problem of generating structured tables from text for NLP tasks like entity-relation extraction, proposing a framework with a permutation-based decoder that maximizes log-likelihood across random orders and searches during inference to reduce errors. It achieves state-of-the-art results, outperforming previous methods by up to 15% on several datasets.
The output structure of database-like tables, consisting of values structured in horizontal rows and vertical columns identifiable by name, can cover a wide range of NLP tasks. Following this constatation, we propose a framework for text-to-table neural models applicable to problems such as extraction of line items, joint entity and relation extraction, or knowledge base population. The permutation-based decoder of our proposal is a generalized sequential method that comprehends information from all cells in the table. The training maximizes the expected log-likelihood for a table's content across all random permutations of the factorization order. During the content inference, we exploit the model's ability to generate cells in any order by searching over possible orderings to maximize the model's confidence and avoid substantial error accumulation, which other sequential models are prone to. Experiments demonstrate a high practical value of the framework, which establishes state-of-the-art results on several challenging datasets, outperforming previous solutions by up to 15%.