A Continuous Relaxation of Beam Search for End-to-end Training of Neural Sequence Models

Understand

Beam search is a desirable choice of test-time decoding algorithm for neural sequence models because it potentially avoids search errors made by simpler greedy methods.

• However, typical cross entropy training procedures for these models do not directly consider the behaviour of the final decoding method.
• As a result, for cross-entropy trained models, beam decoding can sometimes yield reduced test performance when compared with greedy decoding.
• In order to train models that can more effectively make use of beam search, we propose a new training procedure that focuses on the final loss metric (e.g.