Thus, the actual objective is, where J MLE (ϕ) is the log likelihood of the generated SQL query given the input utterance and the anonymous utterance, and J ER (θ) is the expected reward of the anonymous utterance given the input utterance (see Eqn (8) for details.)
when optimizing J MLE (ϕ), we use the concatenation of the encoding of the input utterance (denoted as e(x t )) and the encoding of the anonymous utterance (denoted as h(x t )) as the input for the neural semantic parser, i.e., g t = [e(x t ); h(x t )], t ∈ {1, .