  Motivated by the needs of resource constrained dialog policy learning, we
introduce dialog policy via differentiable inductive logic (DILOG). We explore
the tasks of one-shot learning and zero-shot domain transfer with DILOG on
SimDial and MultiWoZ. Using a single representative dialog from the restaurant
domain, we train DILOG on the SimDial dataset and obtain 99+% in-domain test
accuracy. We also show that the trained DILOG zero-shot transfers to all other
domains with 99+% accuracy, proving the suitability of DILOG to slot-filling
dialogs. We further extend our study to the MultiWoZ dataset achieving 90+%
inform and success metrics. We also observe that these metrics are not
capturing some of the shortcomings of DILOG in terms of false positives,
prompting us to measure an auxiliary Action F1 score. We show that DILOG is
100x more data efficient than state-of-the-art neural approaches on MultiWoZ
while achieving similar performance metrics. We conclude with a discussion on
the strengths and weaknesses of DILOG.

Computation and Language;Artificial Intelligence