DiffuSeq: Sequence to Sequence Text Generation with Diffusion Models (2210.08933v3)

Abstract: Recently, diffusion models have emerged as a new paradigm for generative models. Despite the success in domains using continuous signals such as vision and audio, adapting diffusion models to natural language is under-explored due to the discrete nature of texts, especially for conditional generation. We tackle this challenge by proposing DiffuSeq: a diffusion model designed for sequence-to-sequence (Seq2Seq) text generation tasks. Upon extensive evaluation over a wide range of Seq2Seq tasks, we find DiffuSeq achieving comparable or even better performance than six established baselines, including a state-of-the-art model that is based on pre-trained LLMs. Apart from quality, an intriguing property of DiffuSeq is its high diversity during generation, which is desired in many Seq2Seq tasks. We further include a theoretical analysis revealing the connection between DiffuSeq and autoregressive/non-autoregressive models. Bringing together theoretical analysis and empirical evidence, we demonstrate the great potential of diffusion models in complex conditional language generation tasks. Code is available at \url{https://github.com/Shark-NLP/DiffuSeq}

An Analysis of "DiffuSeq: Sequence to Sequence Text Generation with Diffusion Models"

The paper "DiffuSeq: Sequence to Sequence Text Generation with Diffusion Models" addresses the application of diffusion models for sequence-to-sequence (Seq2Seq) text generation tasks. Diffusion models, which have seen significant success in continuous domains like vision and audio, are extended here to the discrete domain of NLP.

Overview of Contributions

The authors propose DiffuSeq, a diffusion model tailored for Seq2Seq tasks. Key contributions include:

1. Model Architecture: DiffuSeq is designed to handle conditional generation tasks without relying on classifiers. It generates text in a non-autoregressive (NAR) manner, addressing Seq2Seq tasks such as dialogue, paraphrasing, and text style transfer.
2. Theoretical Insights: The paper establishes a theoretical connection between DiffuSeq and traditional autoregressive/non-autoregressive models, positioning DiffuSeq as an extension of iterative-NAR models.
3. Empirical Evaluation: DiffuSeq demonstrates comparable or superior performance against various baselines, including state-of-the-art models based on pre-trained LLMs (PLMs).

Methodology

Diffusion Process Adaptation

The challenge of adapting diffusion models to the discrete nature of text is addressed by embedding textual sequences into a continuous space, enabling typical diffusion processes. DiffuSeq uses a forward process involving partial noising, applying Gaussian noise only to the target sequence embeddings. The reverse process involves conditional denoising that does not depend on external classifiers, allowing the model to leverage full-context information.

Training and Inference

To mitigate inefficiencies due to high diversity and diffusion steps in text data, the authors employ importance sampling, optimizing the training process by focusing on more significant diffusion steps. During inference, an anchoring function and Minimum Bayes Risk (MBR) decoding strategies enhance text generation quality.

Results and Implications

The experimental results across four Seq2Seq tasks reveal that DiffuSeq achieves high diversity without sacrificing generation quality, a notable strength over both AR and NAR baselines. In particular, DiffuSeq's performance in maintaining sentence-level diversity is robust, providing a diverse range of potential outputs for given inputs.

Theoretical and Practical Implications

The paper suggests that diffusion models can potentially surpass AR models in sequence modeling given adequate diffusion steps. By offering a diffusion-based approach, the authors open avenues for further exploration in diverse generative tasks, potentially influencing future advancements in machine translation, dialogue systems, and beyond.

Future Prospects

The results indicate that further advancements in diffusion models might focus on refining training efficiency and enhancing the diversity-quality trade-off. Exploring integration with pre-trained models and optimizing inference speed can significantly contribute to practical applications of DiffuSeq in real-world NLP tasks.

In conclusion, by introducing DiffuSeq, the authors present a promising new paradigm for Seq2Seq text generation, leveraging the unique properties of diffusion models to address challenges in NLP tasks effectively. The paper serves as a critical step towards exploring the full potential of generative models in the discrete field of language.