An Equal-Size Hard EM Algorithm for Diverse Dialogue Generation (2209.14627v2)

Abstract: Open-domain dialogue systems aim to interact with humans through natural language texts in an open-ended fashion. Despite the recent success of super large dialogue systems such as ChatGPT, using medium-to-small-sized dialogue systems remains the common practice as they are more lightweight and accessible; however, generating diverse dialogue responses is challenging, especially with smaller models. In this work, we propose an Equal-size Hard Expectation--Maximization (EqHard-EM) algorithm to train a multi-decoder model for diverse dialogue generation. Our algorithm assigns a sample to a decoder in a hard manner and additionally imposes an equal-assignment constraint to ensure that all decoders are well-trained. We provide detailed theoretical analysis to justify our approach. Further, experiments on two large-scale open-domain dialogue datasets verify that our EqHard-EM algorithm generates high-quality diverse responses.

• The paper presents the EqHard-EM algorithm to overcome generic responses by enforcing equal decoder assignments in multi-decoder dialogue systems.
• It employs a shared encoder with multiple decoders using adapter layers, ensuring parameter efficiency for medium to small-sized models.
• Empirical results on Weibo and OpenSubtitles demonstrate significant improvements in BLEU scores and diversity metrics over conventional methods.

An Equal-Size Hard EM Algorithm for Diverse Dialogue Generation

The paper presents a significant development in the field of open-domain dialogue systems by introducing an innovative method to enhance the diversity of dialogue responses, especially when employing medium to small-sized models. The authors address a common challenge in dialogue generation, where traditional models often generate generic responses, by proposing the Equal-size Hard Expectation--Maximization (EqHard-EM) algorithm. This algorithm is designed to train a multi-decoder model, ensuring diversity in generated dialogues.

Key Contributions

1. Alleviating Generic Responses: The paper emphasizes the problem of generic responses in smaller dialogue models due to the one-to-many mapping phenomenon inherent in conversations. Logic dictates that a single context can lead to multiple plausible responses, yet traditional models often fail to represent this adequately, leading to bland or overly-similar outputs.
2. EqHard-EM Algorithm: The proposed EqHard-EM algorithm stands out by addressing limitations inherent in prior EM variants. Unlike the conventional Soft-EM, which may collapse into generating similar outputs across multiple decoders due to 'synchronous-training', or Hard-EM, which suffers from 'non-training collapse', EqHard-EM employs hard assignments while enforcing an equal assignment constraint. This dual approach ensures that each decoder is robustly trained with a balanced subset of data, preventing any single decoder from dominating due to initial advantageous configurations.
3. Theoretical Justification: The authors provide rigorous theoretical analyses to substantiate the EqHard-EM algorithm's principles. This includes demonstrating that with adequate data and sufficiently accurate posterior estimates, decoder assignments will converge to uniformity, ensuring balanced training.
4. Practical Implementation: The neural architecture proposed employs a shared encoder with multiple decoders, emphasizing parameter efficiency through the use of adapter layers instead of full-Transformers for each decoder. This reduces memory demand whilst maintaining performance, a practical advantage when deploying multiple decoders simultaneously.
5. Empirical Validation: Demonstrated on two extensive datasets—Weibo and OpenSubtitles—EqHard-EM significantly outperforms current models in terms of generating both high-quality and diverse responses. The paper reports superior results in various metrics including BLEU scores and diversity indicators such as distinct n-gram and Pairwise-BLEU, underscoring the model's capability to capture a broader range of dialogue modes.

Implications and Future Work

The development of the EqHard-EM algorithm has several key implications:

• Dialogue Systems: By enabling smaller models to produce diverse outputs without needing excessive resources, EqHard-EM makes such systems more accessible and feasible for broader usage, including real-time applications where computational resources are limited.
• Scalability: The algorithm demonstrates scalability in dialogue generation tasks, potentially guiding future methods in dialogue systems where multiple response paths need modeling.
• Adaptability: With further adaptation, the approach can potentially generalize to other generative tasks beyond dialogues, prompting further research into hard assignment EM applications in varying domains.

In conclusion, EqHard-EM represents a well-grounded, efficient approach to diversifying dialogue outputs in constrained environments. Its blend of theoretical and empirical strength promises practical advances not just in dialogue systems, but possibly extending into broader AI-based conversational agents. The authors have set a pathway that encourages future exploration in resource-efficient, diverse dialogue generation algorithms.