Table-to-text Generation by Structure-aware Seq2seq Learning

Understand

Table-to-text generation aims to generate a description for a factual table which can be viewed as a set of field-value records.

• To encode both the content and the structure of a table, we propose a novel structure-aware seq2seq architecture which consists of field-gating encoder and description generator with dual attention.
• In the encoding phase, we update the cell memory of the LSTM unit by a field gate and its corresponding field value in order to incorporate field information into table representation.
• In the decoding phase, dual attention mechanism which contains word level attention and field level attention is proposed to model the semantic relevance between the generated description and the table.