# Cross Copy Network for Dialogue Generation

arXiv: https://arxiv.org/abs/2010.11539 · HTML: https://mlsanity.com/paper/2010.11539/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Oct 2020.

## Built on by (most cited first, 5 of 5)

- [Recent Advances in Deep Learning Based Dialogue Systems: A Systematic Survey](https://mlsanity.com/paper/2105.04387/) (2021)
- [Social Influence Dialogue Systems: A Survey of Datasets and Models For Social Influence Tasks](https://mlsanity.com/paper/2210.05664/) (2022)
- [Mask the Correct Tokens: An Embarrassingly Simple Approach for Error Correction](https://mlsanity.com/paper/2211.13252/) (2022)
- [Towards Standard Criteria for human evaluation of Chatbots: A Survey](https://mlsanity.com/paper/2105.11197/) (2021)
- [A Neural Conversation Generation Model via Equivalent Shared Memory Investigation](https://mlsanity.com/paper/2108.09164/) (2021)

## Abstract (arXiv)

In the past few years, audiences from different fields witness the achievements of sequence-to-sequence models (e.g., LSTM+attention, Pointer Generator Networks, and Transformer) to enhance dialogue content generation. While content fluency and accuracy often serve as the major indicators for model training, dialogue logics, carrying critical information for some particular domains, are often ignored. Take customer service and court debate dialogue as examples, compatible logics can be observed across different dialogue instances, and this information can provide vital evidence for utterance generation. In this paper, we propose a novel network architecture - Cross Copy Networks(CCN) to explore the current dialog context and similar dialogue instances' logical structure simultaneously. Experiments with two tasks, court debate and customer service content generation, proved that the proposed algorithm is superior to existing state-of-art content generation models.