A Hierarchical Latent Structure for Variational Conversation Modeling (1804.03424v2)

Abstract: Variational autoencoders (VAE) combined with hierarchical RNNs have emerged as a powerful framework for conversation modeling. However, they suffer from the notorious degeneration problem, where the decoders learn to ignore latent variables and reduce to vanilla RNNs. We empirically show that this degeneracy occurs mostly due to two reasons. First, the expressive power of hierarchical RNN decoders is often high enough to model the data using only its decoding distributions without relying on the latent variables. Second, the conditional VAE structure whose generation process is conditioned on a context, makes the range of training targets very sparse; that is, the RNN decoders can easily overfit to the training data ignoring the latent variables. To solve the degeneration problem, we propose a novel model named Variational Hierarchical Conversation RNNs (VHCR), involving two key ideas of (1) using a hierarchical structure of latent variables, and (2) exploiting an utterance drop regularization. With evaluations on two datasets of Cornell Movie Dialog and Ubuntu Dialog Corpus, we show that our VHCR successfully utilizes latent variables and outperforms state-of-the-art models for conversation generation. Moreover, it can perform several new utterance control tasks, thanks to its hierarchical latent structure.

• The paper introduces VHCR, a model that integrates global and local latent variables to capture both conversational context and fine utterance details.
• It employs an utterance drop regularization technique to reduce decoder over-reliance on autoregressive patterns in hierarchical RNNs.
• Empirical results on Cornell Movie Dialog and Ubuntu Dialog datasets demonstrate VHCR's improved performance with stable KL divergence and enhanced dialogue quality.

Overview of "A Hierarchical Latent Structure for Variational Conversation Modeling"

The paper under discussion, "A Hierarchical Latent Structure for Variational Conversation Modeling," presents a novel approach to conversation modeling by addressing the persistent challenge of degeneracy in Variational Autoencoders (VAEs) combined with hierarchical Recurrent Neural Networks (RNNs). This work builds upon existing models, particularly the Hierarchical Recurrent Encoder-Decoder (HRED) and its variant with VAEs, VHRED, to propose the Variational Hierarchical Conversation RNN (VHCR).

The degeneration problem, where decoders overlook latent variables and default to using only RNN structures, is identified as a critical issue. The paper attributes this primarily to the excessive expressiveness of hierarchical RNN decoders and the sparsity in training targets when generation is conditioned on context.

Key Contributions

The authors present the VHCR, which integrates two significant modifications: a hierarchical structure of latent variables and an utterance drop regularization technique. The hierarchical component introduces both global and local latent variables—conversational and utterance levels—thereby effectively capturing the broader conversational context while still attending to finer utterance-level details. The utterance drop methodology is proposed to limit the hierarchical RNNs' autoregressive capabilities, thereby encouraging greater dependency on latent variables.

Empirical Results

The performance of VHCR was assessed using the Cornell Movie Dialog and Ubuntu Dialog Corpus datasets. Notably, VHCR outperformed baseline models, including HRED and VHRED variants, across several metrics. The VHCR achieved a stable and significant KL divergence, reflecting its successful utilization of latent variables without auxiliary losses like bag-of-words losses seen in other variants such as VHRED + bow.

The embedding-based similarity metrics and human evaluation studies further corroborate the performance improvements claimed by VHCR. Notably, VHCR allows for latent variable manipulation to control global conversational properties, supporting tasks like utterance interpolation that were previously infeasible with other models.

Implications and Future Prospects

The introduction of a hierarchical latent structure in variational conversation models opens new avenues for preserving the hierarchical dependencies inherent in human dialogues while mitigating the degeneracy problem. The theoretical implications of this hierarchical approach could stimulate further research on computational efficiency and robustness of hierarchical models in NLP.

Looking ahead, future developments might explore more sophisticated latent structures that encapsulate additional dimensions of dialogue, such as emotional tone or speaker intent. These enhancements could lead to more nuanced conversational agents capable of generating contextually rich and varied dialogues.

In summary, this paper presents a significant advancement in conversation modeling by effectively utilizing hierarchical latent structures to overcome existing limitations in VAE applications within this domain. The VHCR model demonstrates potential for improved performance and capabilities, indicating promising directions for future research in conversational AI.