Besides learning the association between data and text from parallel data-text pairs as in previous work, our model attends to relevant external knowledge, encoded as a temporary memory, and combines this knowledge with the context representation of data before generating words.
both infobox and background knowledge facts are encoded and a dual-attention mechanism is proposed to guide the decoder to generate text.