Hierarchical and Self-Attended Sequence Autoencoder.

Area of Science:

• Natural Language Processing
• Machine Learning
• Artificial Intelligence

Background:

• Inferring stochastic latent semantics is crucial for natural language applications but challenging due to posterior collapse in variational inference, where input sequences are disregarded.
• Posterior collapse hinders effective sequential learning by causing estimated latent variables to ignore the input sequence.

Purpose of the Study:

• To develop a novel variational sequence autoencoder (VSAE) that overcomes posterior collapse and learns sufficient latent information for sophisticated sequence representation.
• To enhance natural language understanding and generation by improving variational sequential learning.

Main Methods:

• Proposed a VSAE model integrating complementary encoders (LSTM and pyramid bidirectional LSTM) to capture global and structural dependencies.
• Incorporated a stochastic self-attention mechanism in the recurrent decoder to facilitate interaction between inference and generation.
• Utilized an autoregressive Gaussian prior for latent variables to preserve information bounds and mitigate posterior collapse.

Main Results:

• Demonstrated that VSAE variants substantially improve performance in variational sequential learning.
• Showcased significant enhancements in language modeling, document classification, and summarization tasks.
• Confirmed the effectiveness of the proposed complementary encoders, attention mechanism, and prior in addressing posterior collapse.

Conclusions:

• The developed VSAE effectively addresses the challenge of posterior collapse in sequence modeling.
• The proposed architecture offers a robust framework for learning rich latent representations in sequential data.
• VSAE significantly advances the state-of-the-art in natural language processing tasks requiring deep semantic understanding.