Learning Discourse-level Diversity for Neural Dialog Models using Conditional Variational Autoencoders (1703.10960v3)

Abstract: While recent neural encoder-decoder models have shown great promise in modeling open-domain conversations, they often generate dull and generic responses. Unlike past work that has focused on diversifying the output of the decoder at word-level to alleviate this problem, we present a novel framework based on conditional variational autoencoders that captures the discourse-level diversity in the encoder. Our model uses latent variables to learn a distribution over potential conversational intents and generates diverse responses using only greedy decoders. We have further developed a novel variant that is integrated with linguistic prior knowledge for better performance. Finally, the training procedure is improved by introducing a bag-of-word loss. Our proposed models have been validated to generate significantly more diverse responses than baseline approaches and exhibit competence in discourse-level decision-making.

Learning Discourse-level Diversity for Neural Dialog Models using Conditional Variational Autoencoders

The paper by Zhao, Zhao, and Eskenazi addresses a well-known limitation of neural encoder-decoder models in the domain of open-dialogue generation: their tendency to produce bland, generic responses. This work proposes a novel approach by incorporating Conditional Variational Autoencoders (CVAE) with a focus on capturing discourse-level diversity in conversational agents. The proposed framework introduces latent variables to model a distribution over potential conversational intents, thus enabling the generation of more varied and contextually appropriate responses.

Key Contributions

The contributions of the paper are multifaceted:

1. Conditional Variational Autoencoders (CVAE) for Dialogue: The authors propose the adaptation of CVAE to dialogue generation. Unlike traditional encoder-decoder models, the CVAE framework captures discourse-level variations by incorporating a latent variable, thus modeling the dialog history and meta features (e.g., topic) to generate diverse responses.
2. Knowledge-Guided CVAE (kgCVAE): An enhanced variant of CVAE, the kgCVAE integrates expert linguistic knowledge, such as dialog acts, into the model. This enables better performance and improves interpretability. The kgCVAE uses the predicted dialog acts to regularize the decoder's generation process, enhancing the contextual coherence and specificity of the responses.
3. Training Enhancement with Bag-of-Word Loss: The paper also introduces a bag-of-word (BOW) loss as an auxiliary objective to tackle the vanishing latent variable problem. This ensures that the latent variable captures global information about the target response, leading to more effective training of CVAE and kgCVAE models.

Experimental Setup and Results

The models were evaluated using the Switchboard Corpus, a domain consisting of 2,400 two-sided telephone conversations with transcriptions and dialog act annotations. Several configurations were trained and assessed via automatic metrics such as BLEU, cosine distance of bag-of-word embeddings, and dialog act matching.

Key results include:

• Perplexity and KL Divergence: The proposed CVAE and kgCVAE models outperform the baseline model in terms of perplexity and KL divergence. Specifically, the kgCVAE achieves the lowest perplexity of 16.02 on the test set.
• Precision and Recall: The paper expands on the precision-recall metrics to account for the diversity of responses. While baseline models show consistent precision due to repetitive high-probability responses, CVAE and kgCVAE models demonstrate superior recall, indicating a broader coverage of valid responses.
• Discourse-Level Diversity: The kgCVAE model achieves the highest precision and recall for BLEU scores, indicating high sentence-level and discourse-level diversity.

Implications and Future Work

The implications of this research are substantial for advancing dialog systems:

• Enhanced Response Diversity: By capturing discourse-level intent variations, the latent variable architectures provide a significant improvement over traditional methods, which tend to restrict diversity to word-level variations.
• Integration of Expert Knowledge: The kgCVAE model illustrates a practical method to embed linguistic heuristics within a generative neural framework, thereby bridging the gap between rule-based systems and purely data-driven models.
• Robust Training Techniques: The introduction of bag-of-word loss in training latent variable models underscores the importance of global context in generating meaningful responses.

Looking ahead, the framework proposed in this paper offers several avenues for further research:

• Extended Linguistic Features: Beyond dialog acts, incorporating other linguistic phenomena such as sentiment and named entities could further enhance model performance.
• Data-driven Dialog Management: The discovered latent variables by the recognition network provide a robust foundation for developing data-driven dialog managers capable of autonomously identifying and managing conversational intents.

In conclusion, the paper makes significant strides in addressing the challenge of generating diverse and contextually appropriate responses in neural dialog systems. The use of CVAE and its knowledge-guided variant, coupled with innovative training techniques, positions this work as a critical step towards more sophisticated and human-like conversational agents.