[Paper Review] Regularizing Dialogue Generation by Imitating Implicit Scenarios

Shaoxiong Feng, Xuancheng Ren+4 more|arXiv (Cornell University)|Oct 5, 2020

Topic Modeling43 references4 citations

TL;DR

This paper proposes a scenario-based dialogue generation framework that leverages future conversation history to implicitly model latent context knowledge, improving response relevance and diversity. By using imitation learning to transfer scenario knowledge from a teacher model (with access to future turns) to a student model (limited to dialogue history), the method regularizes response generation without requiring future context during inference, achieving state-of-the-art performance on four benchmarks.

ABSTRACT

Human dialogues are scenario-based and appropriate responses generally relate to the latent context knowledge entailed by the specific scenario. To enable responses that are more meaningful and context-specific, we propose to improve generative dialogue systems from the scenario perspective, where both dialogue history and future conversation are taken into account to implicitly reconstruct the scenario knowledge. More importantly, the conversation scenarios are further internalized using imitation learning framework, where the conventional dialogue model that has no access to future conversations is effectively regularized by transferring the scenario knowledge contained in hierarchical supervising signals from the scenario-based dialogue model, so that the future conversation is not required in actual inference. Extensive evaluations show that our approach significantly outperforms state-of-the-art baselines on diversity and relevance, and expresses scenario-specific knowledge.

Motivation & Objective

• To address the lack of context-specific knowledge in neural dialogue models, which often produce generic or irrelevant responses.
• To model latent conversation scenarios using both dialogue history and future turns, which encode real-world interaction context.
• To bridge the gap between training (with future context) and inference (without future context) by transferring scenario knowledge via imitation learning.
• To improve response diversity and relevance by regularizing the student model using hierarchical supervisory signals from a scenario-aware teacher.
• To demonstrate that implicit scenario knowledge enhances generalization and robustness in open-domain dialogue generation.

Proposed method

• The scenario-based dialogue model is trained on {dialogue history, future conversation} pairs to generate responses, implicitly learning scenario-aware representations.
• A conventional dialogue model (student) is trained to mimic the scenario-based model (teacher) using imitation learning, with supervision at every hidden layer to improve signal propagation.
• The student model is regularized via distillation from the teacher’s intermediate and output layers, reducing exposure bias and smoothing training trajectories.
• Knowledge transfer is enhanced by using multiple teachers, including a pre-trained language model, to enrich prior knowledge and improve performance.
• The framework enables inference without future conversation by internalizing scenario knowledge through imitation, making it practical for real-world deployment.
• The method uses hierarchical distillation to transfer not only final outputs but also intermediate representations, improving generalization and convergence speed.

Experimental results

Research questions

• RQ1 Can future conversation turns be leveraged to implicitly model scenario knowledge that improves response quality in dialogue generation?
• RQ2 How can scenario knowledge derived from future context be transferred to a model that only sees dialogue history during inference?
• RQ3 Does imitation learning with multi-layer supervision improve the generalization and robustness of dialogue generation models?
• RQ4 Can the integration of implicit scenarios and distillation lead to better diversity and relevance compared to existing methods?
• RQ5 How does the use of multiple teachers (e.g., a pre-trained language model) affect performance and knowledge transfer in the framework?

Key findings

• The proposed method, RegDG, achieves state-of-the-art results on four dialogue datasets, outperforming existing baselines in both diversity and relevance metrics.
• On the DailyDialog dataset, adding a pre-trained language model as an additional teacher improved Dist-1 from 1.1 to 1.3, Dist-2 from 4.4 to 5.0, and BLEU from 19.1 to 19.4.
• The model with multiple teachers showed consistent improvements across all metrics except a slight drop in relevance, attributed to the language model’s unconditional generation nature.
• Hard transfer with more modules accelerated convergence, with the validation loss reaching its lowest point faster.
• The ablation study confirmed that hierarchical distillation (multi-layer imitation) is more effective than top-layer-only distillation, reducing the delayed signal problem.
• Extensive analysis demonstrated that imitating implicit scenarios regularizes the student model, guiding it toward a wider local minimum with better generalization performance.