# PoKE: Prior Knowledge Enhanced Emotional Support Conversation with Latent Variable arXiv: https://arxiv.org/abs/2210.12640 ยท HTML: https://mlsanity.com/paper/2210.12640/ ## Key facts - Cited by 5 papers in the map (data through 14 Oct 2026). - Builds on 28 of its 43 references that are in the map. - First posted on arXiv 23 Oct 2022. ## Builds on (most cited first, 28 of 28) - [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017) - [Auto-Encoding Variational Bayes](https://mlsanity.com/paper/1312.6114/) (2013) - [Dense Passage Retrieval for Open-Domain Question Answering](https://mlsanity.com/paper/2004.04906/) (2020) - [Decoupled Weight Decay Regularization](https://mlsanity.com/paper/1711.05101/) (2017) - [A Diversity-Promoting Objective Function for Neural Conversation Models](https://mlsanity.com/paper/1510.03055/) (2015) - [Retrieval Augmentation Reduces Hallucination in Conversation](https://mlsanity.com/paper/2104.07567/) (2021) - [Generating Sentences from a Continuous Space](https://mlsanity.com/paper/1511.06349/) (2015) - [Recipes for building an open-domain chatbot](https://mlsanity.com/paper/2004.13637/) (2020) - [COMET: Commonsense Transformers for Automatic Knowledge Graph Construction](https://mlsanity.com/paper/1906.05317/) (2019) - [Towards Emotional Support Dialog Systems](https://mlsanity.com/paper/2106.01144/) (2021) - [Learning Discourse-level Diversity for Neural Dialog Models using Conditional Variational Autoencoders](https://mlsanity.com/paper/1703.10960/) (2017) - [Emotional Chatting Machine: Emotional Conversation Generation with Internal and External Memory](https://mlsanity.com/paper/1704.01074/) (2017) - [PLATO: Pre-trained Dialogue Generation Model with Discrete Latent Variable](https://mlsanity.com/paper/1910.07931/) (2019) - [Can You Put it All Together: Evaluating Conversational Agents' Ability to Blend Skills](https://mlsanity.com/paper/2004.08449/) (2020) - [Optimus: Organizing Sentences via Pre-trained Modeling of a Latent Space](https://mlsanity.com/paper/2004.04092/) (2020) - [MIME: MIMicking Emotions for Empathetic Response Generation](https://mlsanity.com/paper/2010.01454/) (2020) - [MoEL: Mixture of Empathetic Listeners](https://mlsanity.com/paper/1908.07687/) (2019) - [CEM: Commonsense-aware Empathetic Response Generation](https://mlsanity.com/paper/2109.05739/) (2021) - [MISC: A MIxed Strategy-Aware Model Integrating COMET for Emotional Support Conversation](https://mlsanity.com/paper/2203.13560/) (2022) - [Retrieve and Refine: Improved Sequence Generation Models For Dialogue](https://mlsanity.com/paper/1808.04776/) (2018) - [PLATO-2: Towards Building an Open-Domain Chatbot via Curriculum Learning](https://mlsanity.com/paper/2006.16779/) (2020) - [DialogWAE: Multimodal Response Generation with Conditional Wasserstein Auto-Encoder](https://mlsanity.com/paper/1805.12352/) (2018) - [Towards Making the Most of BERT in Neural Machine Translation](https://mlsanity.com/paper/1908.05672/) (2019) - [DialogVED: A Pre-trained Latent Variable Encoder-Decoder Model for Dialog Response Generation](https://mlsanity.com/paper/2204.13031/) (2022) - [Implicit Deep Latent Variable Models for Text Generation](https://mlsanity.com/paper/1908.11527/) (2019) - [Emotion-aware Chat Machine: Automatic Emotional Response Generation for Human-like Emotional Interaction](https://mlsanity.com/paper/2106.03044/) (2021) - [Towards Empathetic Open-domain Conversation Models: a New Benchmark and Dataset](https://mlsanity.com/paper/1811.00207/) (2018) - [Exemplars-guided Empathetic Response Generation Controlled by the Elements of Human Communication](https://mlsanity.com/paper/2106.11791/) (2021) ## Built on by (most cited first, 5 of 5) - [COOPER: Coordinating Specialized Agents towards a Complex Dialogue Goal](https://mlsanity.com/paper/2312.11792/) (2023) - [A Cognitive Stimulation Dialogue System with Multi-source Knowledge Fusion for Elders with Cognitive Impairment](https://mlsanity.com/paper/2305.08200/) (2023) - [Knowledge-enhanced Memory Model for Emotional Support Conversation](https://mlsanity.com/paper/2310.07700/) (2023) - [AutoPal: Autonomous Adaptation to Users for Personal AI Companionship](https://mlsanity.com/paper/2406.13960/) (2024) - [K-ESConv: Knowledge Injection for Emotional Support Dialogue Systems via Prompt Learning](https://mlsanity.com/paper/2312.10371/) (2023) ## Abstract (arXiv) Emotional support conversation (ESC) task can utilize various support strategies to help people relieve emotional distress and overcome the problem they face, which has attracted much attention in these years. However, most state-of-the-art works rely heavily on external commonsense knowledge to infer the mental state of the user in every dialogue round. Although effective, they may suffer from significant human effort, knowledge update and domain change in a long run. Therefore, in this article, we focus on exploring the task itself without using any external knowledge. We find all existing works ignore two significant characteristics of ESC. (a) Abundant prior knowledge exists in historical conversations, such as the responses to similar cases and the general order of support strategies, which has a great reference value for current conversation. (b) There is a one-to-many mapping relationship between context and support strategy, i.e.multiple strategies are reasonable for a single context. It lays a better foundation for the diversity of generations. Taking into account these two key factors, we propose Prior Knowledge Enhanced emotional support model with latent variable, PoKE. The proposed model fully taps the potential of prior knowledge in terms of exemplars and strategy sequence and then utilizes a latent variable to model the one-to-many relationship of strategy. Furthermore, we introduce a memory schema to incorporate the encoded knowledge into decoder. Experiment results on benchmark dataset show that our PoKE outperforms existing baselines on both automatic evaluation and human evaluation. Compared with the model using external knowledge, PoKE still can make a slight improvement in some metrics. Further experiments prove that abundant prior knowledge is conducive to high-quality emotional support, and a well-learned latent variable is critical to the diversity of generations.