# Target-Guided Open-Domain Conversation

## TL;DR

This paper introduces a method for open-domain chatbots to proactively guide conversations toward specific goals using keyword control and discourse constraints, supported by a new dataset.

## Contribution

It presents a structured approach combining keyword control, turn-level supervision, and discourse constraints for goal-oriented open-domain conversations, along with a new dataset.

## Key findings

- System effectively guides conversations to target topics.
- Significant improvement over baseline methods in quality and goal achievement.
- Human evaluations confirm the naturalness and effectiveness of the system.

## Abstract

Many real-world open-domain conversation applications have specific goals to achieve during open-ended chats, such as recommendation, psychotherapy, education, etc. We study the problem of imposing conversational goals on open-domain chat agents. In particular, we want a conversational system to chat naturally with human and proactively guide the conversation to a designated target subject. The problem is challenging as no public data is available for learning such a target-guided strategy. We propose a structured approach that introduces coarse-grained keywords to control the intended content of system responses. We then attain smooth conversation transition through turn-level supervised learning, and drive the conversation towards the target with discourse-level constraints. We further derive a keyword-augmented conversation dataset for the study. Quantitative and human evaluations show our system can produce meaningful and effective conversations, significantly improving over other approaches.