Neural Lattice-to-Sequence Models for Uncertain Inputs

Understand

The input to a neural sequence-to-sequence model is often determined by an up-stream system, e.g.

• a word segmenter, part of speech tagger, or speech recognizer.
• These up-stream models are potentially error-prone.
• Representing inputs through word lattices allows making this uncertainty explicit by capturing alternative sequences and their posterior probabilities in a compact form.