Best-$k$ Search Algorithm for Neural Text Generation

Understand

Modern natural language generation paradigms require a good decoding strategy to obtain quality sequences out of the model.

• Beam search yields high-quality but low diversity outputs; stochastic approaches suffer from high variance and sometimes low quality, but the outputs tend to be more natural and creative.
• In this work, we propose a deterministic search algorithm balancing both quality and diversity.
• We first investigate the vanilla best-first search (BFS) algorithm and then propose the Best-$k$ Search algorithm.