# Keyword-Guided Neural Conversational Model

arXiv: https://arxiv.org/abs/2012.08383 · HTML: https://mlsanity.com/paper/2012.08383/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Dec 2020.

## Built on by (most cited first, 21 of 21)

- [Prompting and Evaluating Large Language Models for Proactive Dialogues: Clarification, Target-guided, and Non-collaboration](https://mlsanity.com/paper/2305.13626/) (2023)
- [A Survey on Proactive Dialogue Systems: Problems, Methods, and Prospects](https://mlsanity.com/paper/2305.02750/) (2023)
- [ChatGPT versus Traditional Question Answering for Knowledge Graphs: Current Status and Future Directions Towards Knowledge Graph Chatbots](https://mlsanity.com/paper/2302.06466/) (2023)
- [Target-oriented Proactive Dialogue Systems with Personalization: Problem Formulation and Dataset Curation](https://mlsanity.com/paper/2310.07397/) (2023)
- [COSPLAY: Concept Set Guided Personalized Dialogue Generation Across Both Party Personas](https://mlsanity.com/paper/2205.00872/) (2022)
- [Persona-Guided Planning for Controlling the Protagonist's Persona in Story Generation](https://mlsanity.com/paper/2204.10703/) (2022)
- [Dialogue Planning via Brownian Bridge Stochastic Process for Goal-directed Proactive Dialogue](https://mlsanity.com/paper/2305.05290/) (2023)
- [Interpersonal Memory Matters: A New Task for Proactive Dialogue Utilizing Conversational History](https://mlsanity.com/paper/2503.05150/) (2025)
- [Target-Guided Dialogue Response Generation Using Commonsense and Data Augmentation](https://mlsanity.com/paper/2205.09314/) (2022)
- [Target-Guided Open-Domain Conversation Planning](https://mlsanity.com/paper/2209.09746/) (2022)
- [Target-constrained Bidirectional Planning for Generation of Target-oriented Proactive Dialogue](https://mlsanity.com/paper/2403.06063/) (2024)
- [Enhancing Task Bot Engagement with Synthesized Open-Domain Dialog](https://mlsanity.com/paper/2212.10008/) (2022)
- [The Future of Cognitive Strategy-enhanced Persuasive Dialogue Agents: New Perspectives and Trends](https://mlsanity.com/paper/2402.04631/) (2024)
- [KPT: Keyword-guided Pre-training for Grounded Dialog Generation](https://mlsanity.com/paper/2212.01739/) (2022)
- [Data Augmentation for Conversational AI](https://mlsanity.com/paper/2309.04739/) (2023)
- [Sequential Topic Selection Model with Latent Variable for Topic-Grounded Dialogue](https://mlsanity.com/paper/2210.08801/) (2022)
- [ConceptNet infused DialoGPT for Underlying Commonsense Understanding and Reasoning in Dialogue Response Generation](https://mlsanity.com/paper/2209.15109/) (2022)
- [Personalized Topic Selection Model for Topic-Grounded Dialogue](https://mlsanity.com/paper/2406.01988/) (2024)
- [Pseudo-Siamese Network for Planning in Target-Oriented Proactive Dialogues](https://mlsanity.com/paper/2605.20195/) (2026)
- [Planning with Diffusion Models for Target-Oriented Dialogue Systems](https://mlsanity.com/paper/2504.16858/) (2025)
- [Enhancing Target-Guided Proactive Dialogue Systems via Conversational Scenario Modeling and Intent-Keyword Bridging](https://mlsanity.com/paper/2605.11964/) (2026)

## Abstract (arXiv)

We study the problem of imposing conversational goals/keywords on open-domain conversational agents, where the agent is required to lead the conversation to a target keyword smoothly and fast. Solving this problem enables the application of conversational agents in many real-world scenarios, e.g., recommendation and psychotherapy. The dominant paradigm for tackling this problem is to 1) train a next-turn keyword classifier, and 2) train a keyword-augmented response retrieval model. However, existing approaches in this paradigm have two limitations: 1) the training and evaluation datasets for next-turn keyword classification are directly extracted from conversations without human annotations, thus, they are noisy and have low correlation with human judgements, and 2) during keyword transition, the agents solely rely on the similarities between word embeddings to move closer to the target keyword, which may not reflect how humans converse. In this paper, we assume that human conversations are grounded on commonsense and propose a keyword-guided neural conversational model that can leverage external commonsense knowledge graphs (CKG) for both keyword transition and response retrieval. Automatic evaluations suggest that commonsense improves the performance of both next-turn keyword prediction and keyword-augmented response retrieval. In addition, both self-play and human evaluations show that our model produces responses with smoother keyword transition and reaches the target keyword faster than competitive baselines.