# Disentangling Language and Knowledge in Task-Oriented Dialogs

arXiv: https://arxiv.org/abs/1805.01216 · HTML: https://mlsanity.com/paper/1805.01216/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 May 2018.

## Built on by (most cited first, 17 of 17)

- [Annotation Inconsistency and Entity Bias in MultiWOZ](https://mlsanity.com/paper/2105.14150/) (2021)
- [End-to-End Learning of Flowchart Grounded Task-Oriented Dialogs](https://mlsanity.com/paper/2109.07263/) (2021)
- [A Three-Stage Learning Framework for Low-Resource Knowledge-Grounded Dialogue Generation](https://mlsanity.com/paper/2109.04096/) (2021)
- [End-to-end Task-oriented Dialogue: A Survey of Tasks, Methods, and Future Directions](https://mlsanity.com/paper/2311.09008/) (2023)
- [Knowledge-driven Natural Language Understanding of English Text and its Applications](https://mlsanity.com/paper/2101.11707/) (2021)
- [Constraint based Knowledge Base Distillation in End-to-End Task Oriented Dialogs](https://mlsanity.com/paper/2109.07396/) (2021)
- [Conversational Agents: Theory and Applications](https://mlsanity.com/paper/2202.03164/) (2022)
- [Commonsense and Named Entity Aware Knowledge Grounded Dialogue Generation](https://mlsanity.com/paper/2205.13928/) (2022)
- [Autoregressive Entity Generation for End-to-End Task-Oriented Dialog](https://mlsanity.com/paper/2209.08708/) (2022)
- [A Template-guided Hybrid Pointer Network for Knowledge-basedTask-oriented Dialogue Systems](https://mlsanity.com/paper/2106.05830/) (2021)
- [A Comparative Study on Language Models for Task-Oriented Dialogue Systems](https://mlsanity.com/paper/2201.08687/) (2022)
- [InstructTODS: Large Language Models for End-to-End Task-Oriented Dialogue Systems](https://mlsanity.com/paper/2310.08885/) (2023)
- [Synergizing In-context Learning with Hints for End-to-end Task-oriented Dialog Systems](https://mlsanity.com/paper/2405.15585/) (2024)
- [Edge-Enhanced Global Disentangled Graph Neural Network for Sequential Recommendation](https://mlsanity.com/paper/2111.10539/) (2021)
- [CEAR: Cross-Entity Aware Reranker for Knowledge Base Completion](https://mlsanity.com/paper/2104.08741/) (2021)
- [A Unified Framework for Slot based Response Generation in a Multimodal Dialogue System](https://mlsanity.com/paper/2305.17433/) (2023)
- [DKAF: KB Arbitration for Learning Task-Oriented Dialog Systems with Dialog-KB Inconsistencies](https://mlsanity.com/paper/2305.16697/) (2023)

## Abstract (arXiv)

The Knowledge Base (KB) used for real-world applications, such as booking a movie or restaurant reservation, keeps changing over time. End-to-end neural networks trained for these task-oriented dialogs are expected to be immune to any changes in the KB. However, existing approaches breakdown when asked to handle such changes. We propose an encoder-decoder architecture (BoSsNet) with a novel Bag-of-Sequences (BoSs) memory, which facilitates the disentangled learning of the response's language model and its knowledge incorporation. Consequently, the KB can be modified with new knowledge without a drop in interpretability. We find that BoSsNet outperforms state-of-the-art models, with considerable improvements (> 10%) on bAbI OOV test sets and other human-human datasets. We also systematically modify existing datasets to measure disentanglement and show BoSsNet to be robust to KB modifications.