Reproduce “Variational Attention for Sequence-to-Sequence Models” on OpenResearch
Turn your favorite coding agents into research agents that review literature, develop hypotheses, and run experiments.
Universal app for Apple silicon and Intel. Requires macOS 11 or later.
About this paper
Hareesh Bahuleyan, Lili Mou, Olga Vechtomova, Pascal Poupart
Researchers from the University of Waterloo and AdeptMind Research developed a variational attention mechanism for sequence-to-sequence models to address the 'bypassing phenomenon' in variational encoder-decoders (VEDs). This approach increased the diversity of generated sentences in tasks like question generation and dialogue systems while maintaining output quality.
Key insights
- Deterministic components like standard attention can inadvertently disable the variational properties of VEDs by providing sufficient direct information, leading to latent space collapse.
- Introducing stochasticity directly into the attention mechanism through a variational formulation re-activates the latent space, effectively promoting output diversity.
- The strength of the variational attention's KL loss term can be tuned (via hyperparameter "") to balance the trade-off between generated output quality and diversity.
Summary by alphaXiv.
Tip: change arxiv.org to autoarxiv.org in any arXiv abstract link to open the paper here.