Two-Level Model for Table-to-Text Generation

Table-to-text generation involves using natural language to describe a table which has formal structure and valuable information. This paper introduces a two-level encoder-decoder neural model for table-to-text generation. To make the most of the structure which ordinarily is expressed as a set of field-value records and deal with rare words appearing in a table, this study adopts an improved encoder-decoder approach and uses field information to reprocess words in texts as decoding result. In encoder, two LSTM-RNNs used for combining fields and values that one LSTM-RNN gives priority to fields and the other gives first place to values. In decoder, two-level attention mechanism used on states encoded before to get the relation between words in the text and fields in the table and the relation between words in the text and values in the table. At last the decoding result is transformed to real words. The model is experimented on WIKIBIO and WEATHERGOV, and improves the current state-of-the-art BLEU-4 score from 44.89 to 45.77, and from 61.01 to 62.89 respectively.