Variational Auto-Encoder (VAE) has been widely adopted in text generation.
Among many variants, recurrent VAE learns token-wise latent variables with each
conditioned on the preceding ones, which captures sequential variability better
in the era of RNN. However, it is unclear how to incorporate such recurrent
dynamics into the recently dominant Transformer due to its parallelism. In this
work, we propose TRACE, a Transformer-based recurrent VAE structure. TRACE
imposes recurrence on segment-wise latent variables with arbitrarily separated
text segments and constructs the posterior distribution with residual
parameterization. Besides, we design an acceleration method by approximating
idempotent matrices, which allows parallelism while maintaining the conditional
dependence of latent variables. We demonstrate that TRACE could enhance the
entanglement of each segment and preceding latent variables and deduce a
non-zero lower bound of the KL term, providing a theoretical guarantee of
generation diversity. Experiments on two unconditional and one conditional
generation tasks show that TRACE achieves significantly improved diversity
while maintaining satisfactory generation quality.

本文提出了 TRACE，一种用 Transformer 作为基础的循环 VAE 结构，可以增强文本段之间的交错关系，提高生成多样性，并在不降低生成质量的情况下取得明显改进。

循环增强多样性！重新审视变分自编码器中的循环潜变量用于产生多样文本

Recurrence Boosts Diversity! Revisiting Recurrent Latent Variable in Transformer-Based Variational AutoEncoder for Diverse Text Generation

The Variational Autoencoder (VAE) has proven to be an effective model for
producing semantically meaningful latent representations for natural data.
However, it has thus far seen limited application to sequential data, and, as
we demonstrate, existing recurrent VAE models have difficulty modeling
sequences with long-term structure. To address this issue, we propose the use
of a hierarchical decoder, which first outputs embeddings for subsequences of
the input and then uses these embeddings to generate each subsequence
independently. This structure encourages the model to utilize its latent code,
thereby avoiding the "posterior collapse" problem, which remains an issue for
recurrent VAEs. We apply this architecture to modeling sequences of musical
notes and find that it exhibits dramatically better sampling, interpolation,
and reconstruction performance than a "flat" baseline model. An implementation
of our "MusicVAE" is available online at this http URL

本文介绍了一种新的变分自编码器模型 MusicVAE，利用层次化解码器结构用于序列数据建模，解决了长程序列结构状态建模的问题，并实现了更好的采样、插值和重构表现。