A Hierarchical Latent Variable Encoder-Decoder Model for Generating Dialogues (1605.06069v3)

Abstract: Sequential data often possesses a hierarchical structure with complex dependencies between subsequences, such as found between the utterances in a dialogue. In an effort to model this kind of generative process, we propose a neural network-based generative architecture, with latent stochastic variables that span a variable number of time steps. We apply the proposed model to the task of dialogue response generation and compare it with recent neural network architectures. We evaluate the model performance through automatic evaluation metrics and by carrying out a human evaluation. The experiments demonstrate that our model improves upon recently proposed models and that the latent variables facilitate the generation of long outputs and maintain the context.

• The paper introduces the VHRED model, which integrates hierarchical structures and latent variables to capture local and global dialogue dependencies.
• The model employs variational training to effectively manage high-entropy dialogue sequences and outperforms traditional RNN and HRED approaches in both quantitative and human evaluations.
• Experiments on Twitter and Ubuntu dialogue datasets demonstrate that VHRED generates diverse, coherent responses that enhance context understanding in dialogue systems.

A Hierarchical Latent Variable Encoder-Decoder Model for Generating Dialogues

This paper introduces a novel hierarchical latent variable neural network architecture for the purpose of generating dialogues, addressing the limitations of traditional Recurrent Neural Networks (RNNs) and their extensions when applied to dialogue response generation tasks. The authors propose the Latent Variable Hierarchical Recurrent Encoder-Decoder (VHRED) model to better capture the hierarchical structure inherent in dialogue sequences.

Key Contributions

The primary contributions of this work are as follows:

1. Hierarchical Model Architecture: The proposed VHRED model incorporates a hierarchical structure to manage the multiple levels of variability present in dialogue data. This allows the model to capture both local (within-utterance) and global (across-utterances) dependencies, which are crucial for generating coherent and contextually appropriate responses in a dialogue.
2. Latent Variable Integration: By introducing stochastic latent variables that span multiple time steps, the model is capable of better capturing long-term dependencies within the data. This is in contrast to traditional RNN-based approaches which typically struggle with maintaining context over longer sequences.
3. Variational Training: The training procedure employs a variational lower-bound on the log-likelihood, enhancing the model's ability to manage high-entropy sequences. This allows for more effective learning and generation of diverse, information-rich responses.

Methodology

The VHRED model is an extension of the Hierarchical Recurrent Encoder-Decoder (HRED). It integrates latent stochastic variables sampled from a prior distribution, which are then used to condition the generation of both the intra- and inter-utterance sequences. This leads to a two-level generation process where:

1. Context RNN encodes the sequence of dialogue turns.
2. Decoder RNN generates words within each turn, conditioned on the context and the sampled latent variables.

Experimental Setup

The model was tested on two large dialogue datasets:

• Twitter Dialogue Corpus: Consisting of casual, social interactions.
• Ubuntu Dialogue Corpus: Providing technical support interactions in an IRC channel.

For evaluation, the authors utilized both quantitative metrics and human evaluations via Amazon Mechanical Turk (AMT). The model's performance was compared against several baselines:

• LSTM-based approaches
• Traditional HRED models
• Non-neural, retrieval-based methods

Results

The VHRED model demonstrated several significant improvements:

• Human Evaluation: In AMT studies, VHRED was consistently preferred over other models, especially in scenarios with longer contexts.
• Metric-Based Evaluation: Various embedding-based metrics showed that VHRED responses had a higher semantic similarity to true responses and context. These metrics included:
  • Embedding Average
  • Embedding Extrema
  • Embedding Greedy

Additionally, VHRED generated longer, more diverse, and information-rich responses compared to its counterparts. This is quantitatively supported by higher entropy measurements in the generated responses, indicating more varied and meaningful dialogue generation.

Implications and Future Directions

The results highlight the importance of incorporating hierarchical structures and latent variables in dialogue generation models. These enhancements address the limitations of flat, deterministic RNN architectures, enabling more coherent and contextually appropriate dialogues.

The theoretical implications are significant: the integration of stochastic latent variables bridges the gap between short-term and long-term dependencies in sequential data. Practically, this leads to models that better understand and generate dialogue, which has a myriad of applications in customer service, technical support, and virtual assistants.

Future research can explore the extension of this model to other hierarchical generative tasks such as document-level machine translation and multi-sentence summarization. Further exploration could also optimize the variational training process to enhance the model's stability and efficiency in learning rich latent structures.

Overall, VHRED presents a promising advancement in the field of natural language processing by addressing key challenges in dialogue response generation with a robust, hierarchical approach.