# Commonsense and Named Entity Aware Knowledge Grounded Dialogue Generation

arXiv: https://arxiv.org/abs/2205.13928 · HTML: https://mlsanity.com/paper/2205.13928/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 31 of its 54 references that are in the map.
- First posted on arXiv 27 May 2022.

## Builds on (most cited first, 30 of 31)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (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)
- [DialoGPT: Large-Scale Generative Pre-training for Conversational Response Generation](https://mlsanity.com/paper/1911.00536/) (2019)
- [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)
- [How NOT To Evaluate Your Dialogue System: An Empirical Study of Unsupervised Evaluation Metrics for Dialogue Response Generation](https://mlsanity.com/paper/1603.08023/) (2016)
- [A Persona-Based Neural Conversation Model](https://mlsanity.com/paper/1603.06155/) (2016)
- [Learning Discourse-level Diversity for Neural Dialog Models using Conditional Variational Autoencoders](https://mlsanity.com/paper/1703.10960/) (2017)
- [Building End-To-End Dialogue Systems Using Generative Hierarchical Neural Network Models](https://mlsanity.com/paper/1507.04808/) (2015)
- [A Neural Conversational Model](https://mlsanity.com/paper/1506.05869/) (2015)
- [A Hierarchical Latent Variable Encoder-Decoder Model for Generating Dialogues](https://mlsanity.com/paper/1605.06069/) (2016)
- [A Knowledge-Grounded Neural Conversation Model](https://mlsanity.com/paper/1702.01932/) (2017)
- [Neural Responding Machine for Short-Text Conversation](https://mlsanity.com/paper/1503.02364/) (2015)
- [Knowledge-Grounded Dialogue Generation with Pre-trained Language Models](https://mlsanity.com/paper/2010.08824/) (2020)
- [A Dataset for Document Grounded Conversations](https://mlsanity.com/paper/1809.07358/) (2018)
- [Coreferential Reasoning Learning for Language Representation](https://mlsanity.com/paper/2004.06870/) (2020)
- [Low-Resource Knowledge-Grounded Dialogue Generation](https://mlsanity.com/paper/2002.10348/) (2020)
- [Pointing the Unknown Words](https://mlsanity.com/paper/1603.08148/) (2016)
- [Incremental Transformer with Deliberation Decoder for Document Grounded Conversations](https://mlsanity.com/paper/1907.08854/) (2019)
- [Pre-trained Language Model Representations for Language Generation](https://mlsanity.com/paper/1903.09722/) (2019)
- [GECOR: An End-to-End Generative Ellipsis and Co-reference Resolution Model for Task-Oriented Dialogue](https://mlsanity.com/paper/1909.12086/) (2019)
- [DyKgChat: Benchmarking Dialogue Generation Grounding on Dynamic Knowledge Graphs](https://mlsanity.com/paper/1910.00610/) (2019)
- [Conversing by Reading: Contentful Neural Conversation with On-demand Machine Reading](https://mlsanity.com/paper/1906.02738/) (2019)
- [ReCoSa: Detecting the Relevant Contexts with Self-Attention for Multi-turn Dialogue Generation](https://mlsanity.com/paper/1907.05339/) (2019)
- [Augmenting End-to-End Dialog Systems with Commonsense Knowledge](https://mlsanity.com/paper/1709.05453/) (2017)
- [Disentangling Language and Knowledge in Task-Oriented Dialogs](https://mlsanity.com/paper/1805.01216/) (2018)
- [Multi-level Memory for Task Oriented Dialogs](https://mlsanity.com/paper/1810.10647/) (2018)
- [Pretrained Language Models for Dialogue Generation with Multiple Input Sources](https://mlsanity.com/paper/2010.07576/) (2020)

## Built on by (most cited first, 8 of 8)

- [A Survey of Knowledge Enhanced Pre-trained Language Models](https://mlsanity.com/paper/2211.05994/) (2022)
- [Bridging Cultural Nuances in Dialogue Agents through Cultural Value Surveys](https://mlsanity.com/paper/2401.10352/) (2024)
- [Commonsense Reasoning for Conversational AI: A Survey of the State of the Art](https://mlsanity.com/paper/2302.07926/) (2023)
- [Automated Fact-Checking in Dialogue: Are Specialized Models Needed?](https://mlsanity.com/paper/2311.08195/) (2023)
- [A Knowledge Plug-and-Play Test Bed for Open-domain Dialogue Generation](https://mlsanity.com/paper/2403.03496/) (2024)
- [Leveraging Explicit Reasoning for Inference Integration in Commonsense-Augmented Dialogue Models](https://mlsanity.com/paper/2406.09138/) (2024)
- [Multi-turn Conversational AI from Text to Multimodal Interaction: Data, Models, Evaluation, and Open Challenges](https://mlsanity.com/paper/2608.17605/) (2026)
- [Exploring the Impact of Human Evaluator Group on Chat-Oriented Dialogue Evaluation](https://mlsanity.com/paper/2309.07998/) (2023)

## Abstract (arXiv)

Grounding dialogue on external knowledge and interpreting linguistic patterns in dialogue history context, such as ellipsis, anaphora, and co-references is critical for dialogue comprehension and generation. In this paper, we present a novel open-domain dialogue generation model which effectively utilizes the large-scale commonsense and named entity based knowledge in addition to the unstructured topic-specific knowledge associated with each utterance. We enhance the commonsense knowledge with named entity-aware structures using co-references. Our proposed model utilizes a multi-hop attention layer to preserve the most accurate and critical parts of the dialogue history and the associated knowledge. In addition, we employ a Commonsense and Named Entity Enhanced Attention Module, which starts with the extracted triples from various sources and gradually finds the relevant supporting set of triples using multi-hop attention with the query vector obtained from the interactive dialogue-knowledge module. Empirical results on two benchmark dataset demonstrate that our model significantly outperforms the state-of-the-art methods in terms of both automatic evaluation metrics and human judgment. Our code is publicly available at hrefhttps://github.com/deekshaVarshney/CNTFhttps://github.com/deekshaVarshney/CNTF; hrefhttps://www.iitp.ac.in/~ai-nlp-ml/resources/codes/CNTF.ziphttps://www.iitp.ac.in/-ai-nlp-ml/resources/ codes/CNTF.zip.