# Goal-Embedded Dual Hierarchical Model for Task-Oriented Dialogue Generation

arXiv: https://arxiv.org/abs/1909.09220 · HTML: https://mlsanity.com/paper/1909.09220/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Sep 2019.

## Built on by (most cited first, 1 of 1)

- [Improving Prediction Backward-Compatiblility in NLP Model Upgrade with Gated Fusion](https://mlsanity.com/paper/2302.02080/) (2023)

## Abstract (arXiv)

Hierarchical neural networks are often used to model inherent structures within dialogues. For goal-oriented dialogues, these models miss a mechanism adhering to the goals and neglect the distinct conversational patterns between two interlocutors. In this work, we propose Goal-Embedded Dual Hierarchical Attentional Encoder-Decoder (G-DuHA) able to center around goals and capture interlocutor-level disparity while modeling goal-oriented dialogues. Experiments on dialogue generation, response generation, and human evaluations demonstrate that the proposed model successfully generates higher-quality, more diverse and goal-centric dialogues. Moreover, we apply data augmentation via goal-oriented dialogue generation for task-oriented dialog systems with better performance achieved.