Open-Domain Table-to-Text Generation based on Seq2seq

Table-to-text generation involves using natural language to describe a table which has formal structure and valuable information. Open-domain table-to-text especially refers to table-to-text generation for open domain. This paper introduces a theme model based on seq2seq for open-domain table-to-text generation. To deal with the problem of out-of-vocabulary and make the most of the internal correlation within table and the relevance between table and text, this study adopts an improved encoder-decoder approach and a method associating table and text. In addition, this paper improves the beam search method for the inference of the model. The model is experimented on WIKITABLETEXT, and improves the current state-of-the-art BLEU-4 score from 38.23 to 38.71.