Jointly Learning Semantic Parser and Natural Language Generator via Dual Information Maximization
Table 5: Ablation study with full training set by freezing ( ) model parameters for generator or parser during learning. Darker indicates higher values.
['Parser', 'Super', 'Gen.', 'DIM']
Ablation Study. We conduct ablation studies by training DIM with the parameters of parser or generator frozen. As anticipated, for both of parsing and generation, when the dual model is frozen, the performance of the primal model degrades. This again demonstrates DIM’s effectiveness of jointly optimizing both tasks. Intuitively, jointly updating both the primal and dual models allows a better learned dual model to provide high-quality learning signals, leading to an improved lower bound for the primal. As a result, freezing parameters of the dual model has a negative impact on the learning signal quality, which affects primal model learning.