We implement a linear gate with encodings of the input utterance and aggregated encodings of column names as input: where W gate stands for learnable parameters, C Q t is the aggregated encoding of column names, and is the concatenation of Q t and C Q t .
to obtain the most relevant column names for the t-th token in the input utterance, we leverage attention mechanism (Bahdanau et al., 2015) towards column names {C 1 , .