# Know Deeper: Knowledge-Conversation Cyclic Utilization Mechanism for Open-domain Dialogue Generation

arXiv: https://arxiv.org/abs/2107.07771 · HTML: https://mlsanity.com/paper/2107.07771/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 20 references that are in the map.
- First posted on arXiv 16 Jul 2021.

## Builds on (most cited first, 18 of 18)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014)
- [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014)
- [Sequence to Sequence Learning with Neural Networks](https://mlsanity.com/paper/1409.3215/) (2014)
- [A Diversity-Promoting Objective Function for Neural Conversation Models](https://mlsanity.com/paper/1510.03055/) (2015)
- [Personalizing Dialogue Agents: I have a dog, do you have pets too?](https://mlsanity.com/paper/1801.07243/) (2018)
- [Wizard of Wikipedia: Knowledge-Powered Conversational agents](https://mlsanity.com/paper/1811.01241/) (2018)
- [Deep Reinforcement Learning for Dialogue Generation](https://mlsanity.com/paper/1606.01541/) (2016)
- [A Neural Conversational Model](https://mlsanity.com/paper/1506.05869/) (2015)
- [What makes a good conversation? How controllable attributes affect human judgments](https://mlsanity.com/paper/1902.08654/) (2019)
- [Neural Responding Machine for Short-Text Conversation](https://mlsanity.com/paper/1503.02364/) (2015)
- [A Dataset for Document Grounded Conversations](https://mlsanity.com/paper/1809.07358/) (2018)
- [Learning to Select Knowledge for Response Generation in Dialog Systems](https://mlsanity.com/paper/1902.04911/) (2019)
- [Hierarchical Recurrent Attention Network for Response Generation](https://mlsanity.com/paper/1701.07149/) (2017)
- [Generative Deep Neural Networks for Dialogue: A Short Review](https://mlsanity.com/paper/1611.06216/) (2016)
- [Multi-Task Learning for Speaker-Role Adaptation in Neural Conversation Models](https://mlsanity.com/paper/1710.07388/) (2017)
- [Assigning personality/identity to a chatting machine for coherent conversation generation](https://mlsanity.com/paper/1706.02861/) (2017)
- [Know More about Each Other: Evolving Dialogue Strategy via Compound Assessment](https://mlsanity.com/paper/1906.00549/) (2019)

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

- [Mind the Knowledge Gap: A Survey of Knowledge-enhanced Dialogue Systems](https://mlsanity.com/paper/2212.09252/) (2022)

## Abstract (arXiv)

End-to-End intelligent neural dialogue systems suffer from the problems of generating inconsistent and repetitive responses. Existing dialogue models pay attention to unilaterally incorporating personal knowledge into the dialog while ignoring the fact that incorporating the personality-related conversation information into personal knowledge taken as the bilateral information flow boosts the quality of the subsequent conversation. Besides, it is indispensable to control personal knowledge utilization over the conversation level. In this paper, we propose a conversation-adaption multi-view persona aware response generation model that aims at enhancing conversation consistency and alleviating the repetition from two folds. First, we consider conversation consistency from multiple views. From the view of the persona profile, we design a novel interaction module that not only iteratively incorporates personalized knowledge into each turn conversation but also captures the personality-related information from conversation to enhance personalized knowledge semantic representation. From the view of speaking style, we introduce the speaking style vector and feed it into the decoder to keep the speaking style consistency. To avoid conversation repetition, we devise a coverage mechanism to keep track of the activation of personal knowledge utilization. Experiments on both automatic and human evaluation verify the superiority of our model over previous models.