Controlling Style in Generated Dialogue

The evolution of dialogue systems has transitioned from rule-based and retrieval models to large-scale generative architectures like GPT-3 and DialoGPT, which excel in producing fluent responses. Early efforts focused on improving relevance and coherence, with notable works such as Zhang et al. (2018) introducing persona-based models and Dinan et al. (2019) integrating external knowledge. Despite these improvements, maintaining consistent style and personality across diverse contexts remains challenging. Prior approaches like Keskar et al. (2019) employed control codes for style modulation, but their scope was limited to small style sets. Recent research emphasizes scaling models and incorporating controllability mechanisms, yet comprehensive solutions for multi-style, multi-dimensional regulation in open-domain dialogue are lacking. This work builds upon these foundations, aiming to extend style control to hundreds of styles with high fidelity.

The core issue is enabling large dialogue models to generate responses that not only are relevant and fluent but also adhere to specific stylistic attributes. Existing methods either lack scalability, produce inconsistent style transfer, or significantly impair response quality. The challenge involves balancing style accuracy, diversity, and conversational coherence, especially when dealing with a broad style space (~200 styles). Moreover, the reliance on limited labeled data hampers the ability to generalize style control across various datasets and real-world scenarios. Addressing these bottlenecks is crucial for deploying personalized, emotionally intelligent dialogue agents in practical applications.

The paper introduces a multi-architecture framework integrating retrieval, iterative refinement, and conditioned generation for style control in dialogue. Key innovations include: 1) leveraging a style classifier trained on Image-Chat to annotate large unlabeled datasets, enabling semi-supervised learning; 2) adapting the retrieve-and-style-transfer approach to dialogue, balancing relevance and style; 3) refining PPLM for dialogue-specific fine-grained style manipulation without retraining the base model; 4) implementing conditioned fine-tuning with style tokens, achieving high style accuracy with efficient inference. These methods collectively push the boundary of controllability in large-scale dialogue models, offering scalable, effective solutions for diverse style management.

• �� Train a style classifier on Image-Chat to label unlabeled dialogue datasets, creating a semi-supervised training set.
• �� Fine-tune a 2.7B parameter GPT-based model on labeled data with style tokens appended to context for conditioned generation.
• �� Implement retrieve-and-style-transfer: retrieve relevant responses, append style labels, and generate new responses conditioned on style.
• �� Adapt PPLM by integrating a style classifier into the inference process, iteratively refining responses to match target styles.
• �� Evaluate models on datasets like ConvAI2, Wizard of Wikipedia, and BST, measuring style accuracy, relevance, and fluency.
• �� Conduct ablation studies to compare architecture performance and analyze trade-offs in computational cost and control fidelity.

Experiments involved training models on datasets with diverse styles, including IC, D, and BST, and evaluating style matching accuracy, relevance, and naturalness. Style classifier accuracy was validated on IC (~13%), and models were assessed via automated metrics and human judgments. The C100 model achieved 16.7% style match accuracy, outperforming RnST and PPLM. Ablation studies examined the impact of training data size, style label quality, and architecture complexity. Results confirmed that conditioned fine-tuning offers the best trade-off between style control and response quality, with significant improvements over baseline models.

• �� The conditioned generation model C100 achieved 16.7% style matching accuracy, outperforming RnST (15.8%) and PPLM, while maintaining relevance and fluency.
• �� Style control was effective across a broad set (~200 styles), with minimal degradation of conversational metrics.
• �� Semi-supervised style labeling via classifier augmentation enabled scaling to large, unlabeled datasets.
• �� Limitations included classifier noise and challenges in controlling subtle styles, indicating room for multimodal and more precise control enhancements.

• �� Deployment in personalized virtual assistants, enabling tailored tone and persona.
• �� Enhancing customer service bots with consistent, engaging styles.
• �� Future integration with multimodal cues (images, audio) for richer, more natural interactions, applicable in entertainment and education sectors.

• �� Style classifier accuracy remains imperfect, especially for nuanced styles, affecting control precision.
• �� Computational costs, especially for iterative methods like PPLM, limit real-time deployment.
• �� Current reliance on text-based labels restricts style richness; multimodal signals could improve realism.