# Resource Constrained Dialog Policy Learning via Differentiable Inductive Logic Programming

arXiv: https://arxiv.org/abs/2011.05457 · HTML: https://mlsanity.com/paper/2011.05457/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Nov 2020.

## Built on by (most cited first, 2 of 2)

- [A Neuro-Symbolic Approach to Monitoring Salt Content in Food](https://mlsanity.com/paper/2404.01182/) (2024)
- [Every time I fire a conversational designer, the performance of the dialog system goes down](https://mlsanity.com/paper/2109.13029/) (2021)

## Abstract (arXiv)

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.