# Unsupervised Learning of KB Queries in Task-Oriented Dialogs

arXiv: https://arxiv.org/abs/2005.00123 · HTML: https://mlsanity.com/paper/2005.00123/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Apr 2020.

## Built on by (most cited first, 6 of 6)

- [End-to-End Neuro-Symbolic Architecture for Image-to-Image Reasoning Tasks](https://mlsanity.com/paper/2106.03121/) (2021)
- [Variational Learning for Unsupervised Knowledge Grounded Dialogs](https://mlsanity.com/paper/2112.00653/) (2021)
- [Learning Interpretable Latent Dialogue Actions With Less Supervision](https://mlsanity.com/paper/2209.11128/) (2022)
- [Mix-and-Match: Scalable Dialog Response Retrieval using Gaussian Mixture Embeddings](https://mlsanity.com/paper/2204.02710/) (2022)
- [Joint Reasoning on Hybrid-knowledge sources for Task-Oriented Dialog](https://mlsanity.com/paper/2210.07295/) (2022)
- [DKAF: KB Arbitration for Learning Task-Oriented Dialog Systems with Dialog-KB Inconsistencies](https://mlsanity.com/paper/2305.16697/) (2023)

## Abstract (arXiv)

Task-oriented dialog (TOD) systems often need to formulate knowledge base (KB) queries corresponding to the user intent and use the query results to generate system responses. Existing approaches require dialog datasets to explicitly annotate these KB queries -- these annotations can be time consuming, and expensive. In response, we define the novel problems of predicting the KB query and training the dialog agent, without explicit KB query annotation. For query prediction, we propose a reinforcement learning (RL) baseline, which rewards the generation of those queries whose KB results cover the entities mentioned in subsequent dialog. Further analysis reveals that correlation among query attributes in KB can significantly confuse memory augmented policy optimization (MAPO), an existing state of the art RL agent. To address this, we improve the MAPO baseline with simple but important modifications suited to our task. To train the full TOD system for our setting, we propose a pipelined approach: it independently predicts when to make a KB query (query position predictor), then predicts a KB query at the predicted position (query predictor), and uses the results of predicted query in subsequent dialog (next response predictor). Overall, our work proposes first solutions to our novel problem, and our analysis highlights the research challenges in training TOD systems without query annotation.