Best of Both Worlds: Transferring Knowledge from Discriminative Learning to a Generative Visual Dialog Model (1706.01554v2)

Abstract: We present a novel training framework for neural sequence models, particularly for grounded dialog generation. The standard training paradigm for these models is maximum likelihood estimation (MLE), or minimizing the cross-entropy of the human responses. Across a variety of domains, a recurring problem with MLE trained generative neural dialog models (G) is that they tend to produce 'safe' and generic responses ("I don't know", "I can't tell"). In contrast, discriminative dialog models (D) that are trained to rank a list of candidate human responses outperform their generative counterparts; in terms of automatic metrics, diversity, and informativeness of the responses. However, D is not useful in practice since it cannot be deployed to have real conversations with users. Our work aims to achieve the best of both worlds -- the practical usefulness of G and the strong performance of D -- via knowledge transfer from D to G. Our primary contribution is an end-to-end trainable generative visual dialog model, where G receives gradients from D as a perceptual (not adversarial) loss of the sequence sampled from G. We leverage the recently proposed Gumbel-Softmax (GS) approximation to the discrete distribution -- specifically, an RNN augmented with a sequence of GS samplers, coupled with the straight-through gradient estimator to enable end-to-end differentiability. We also introduce a stronger encoder for visual dialog, and employ a self-attention mechanism for answer encoding along with a metric learning loss to aid D in better capturing semantic similarities in answer responses. Overall, our proposed model outperforms state-of-the-art on the VisDial dataset by a significant margin (2.67% on recall@10). The source code can be downloaded from https://github.com/jiasenlu/visDial.pytorch.

An In-Depth Review of Knowledge Transfer from Discriminative to Generative Visual Dialog Models

The paper "Best of Both Worlds: Transferring Knowledge from Discriminative Learning to a Generative Visual Dialog Model" addresses a compelling issue in the development of dialog systems. In essence, it seeks a solution to the inherent limitations faced by both generative and discriminative models in the context of visual dialog tasks. The authors propose an innovative framework leveraging the strengths of both paradigms, aiming to enhance the practical utility and effectiveness of generative models via knowledge transfer from discriminative models.

Problem Statement and Context

Generative models, commonly trained using Maximum Likelihood Estimation (MLE), tend to produce safe and generic responses, which detract from the richness and engagement of a dialog. Discriminative models, although more performant in scoring plausible candidate responses, lack practical utility in real-time dialog due to their reliance on set answer options. The focus of this paper is thus to retain the benefits of discriminative models while empowering generative models to produce more informative and diverse responses.

Methodology: A Novel Training Paradigm

The authors introduce a unique framework for training generative visual dialog models. The core of this approach lies in allowing the generative model to receive gradients from the discriminative model, treating the latter's output as a perceptual loss. This is operationalized by integrating the Gumbel-Softmax (GS) approximation with a Recurrent Neural Network (RNN), empowering the model with end-to-end differentiability through the straight-through gradient estimator.

Additionally, enhancements to the model's architecture are implemented, including a novel encoder that utilizes dual memory banks for visual and textual inputs, benefiting from a self-attention mechanism. This design, combined with a sophisticated loss function, facilitates effective knowledge transfer by capturing semantic similarities in dialog responses.

Results and Discussion

The proposed model demonstrates significant performance improvements on the VisDial dataset, achieving a notable margin of 2.67% in recall@10 over previous state-of-the-art methods. A meticulous comparison of model variants reveals the discernible advantages of knowledge transfer over mere architectural enhancements. The research highlights the potential of metric learning and the introduction of a self-attentive mechanism for answer encoding as critical components in refining the capabilities of discriminative models and by extension, transferring these improvements to generative models.

Theoretical and Practical Implications

Theoretically, this approach suggests broader applicability of adversarial-style training, where the perceptual nuance of discriminative frameworks is leveraged to refine generative outputs. Practically, the methodology opens avenues for deploying more engaging and context-aware dialog systems in real-world applications, where maintaining the engagement of human users is critical.

Future Directions

While the results are promising, further refinements in the training stability and efficiency of the proposed knowledge transfer mechanism could facilitate its adoption into diverse dialog systems. Additionally, extending the framework to multimodal interactions beyond visual and text stimuli or exploring its application in more complex dialog scenarios could provide further enhancements and insights.

In summary, this paper presents a thought-provoking advancement in dialog models, laying the groundwork for more dynamic and versatile AI systems capable of simulating human-like conversational interactions. This work not only progresses visual dialog models but also posits a methodological bridge that can be leveraged across various AI applications to harmonize generative and discriminative paradigms.