[Paper Review] Online Sequence-to-Sequence Reinforcement Learning for Open-Domain Conversational Agents

Nabiha Asghar, Pascal Poupart+2 more|arXiv (Cornell University)|Dec 12, 2016

Topic Modeling10 citations

TL;DR

This paper proposes an online, end-to-end neural sequence-to-sequence model for open-domain conversational agents that combines offline supervised learning with online human-in-the-loop active learning using one-character user feedback and hamming-diverse beam search. The method enables personalized dialogue generation with customized personas, moods, and styles, achieving semantically relevant and engaging responses without hand-crafted reward functions.

ABSTRACT

We propose an online, end-to-end, neural generative conversational model for open-domain dialogue. It is trained using a unique combination of offline two-phase supervised learning and online human-in-the-loop active learning. While most existing research proposes offline supervision or hand-crafted reward functions for online reinforcement, we devise a novel interactive learning mechanism based on hamming-diverse beam search for response generation and one-character user-feedback at each step. Experiments show that our model inherently promotes the generation of semantically relevant and interesting responses, and can be used to train agents with customized personas, moods and conversational styles.

Motivation & Objective

• To develop a trainable, end-to-end conversational agent that adapts to user preferences in real time.
• To overcome limitations of offline supervision and hand-crafted reward functions in dialogue policy learning.
• To enable personalized dialogue systems with customizable personas, moods, and conversational styles.
• To design an interactive learning mechanism that uses minimal user feedback (one-character responses) to guide response generation.
• To promote semantic relevance and diversity in generated responses through a novel beam search strategy.

Proposed method

• The model uses a two-phase training process: first, supervised fine-tuning on offline dialogue data, followed by online active learning.
• Online learning employs one-character user feedback at each turn to provide immediate, minimal supervision.
• A hamming-diverse beam search is used during response generation to explore semantically diverse response candidates.
• The interaction loop integrates human feedback into the policy update, enabling real-time adaptation of the generative policy.
• The method avoids hand-designed reward functions by relying on direct human feedback to shape response quality.
• The model is trained end-to-end, allowing joint optimization of response generation and policy adaptation.

Experimental results

Research questions

• RQ1 Can online, human-in-the-loop learning improve response quality in open-domain dialogue systems compared to offline supervision?
• RQ2 How effective is one-character feedback in guiding the generation of semantically relevant and diverse responses?
• RQ3 Can the proposed method personalize dialogue agents with distinct personas, moods, and conversational styles?
• RQ4 Does hamming-diverse beam search enhance response diversity and relevance without requiring complex reward shaping?
• RQ5 Can end-to-end training with minimal feedback achieve strong performance without hand-crafted reward functions?

Key findings

• The model generates semantically relevant and engaging responses without relying on hand-crafted reward functions.
• One-character user feedback effectively guides the learning process, enabling real-time adaptation of the dialogue policy.
• The use of hamming-diverse beam search promotes diversity in generated responses while maintaining relevance.
• The system successfully supports training agents with customized personas, moods, and conversational styles.
• The combination of offline pre-training and online active learning leads to improved response quality over purely offline methods.
• The model demonstrates inherent capability to generate interesting and contextually appropriate responses through interactive learning.