Self-Attentional Models for Lattice Inputs
Table 4: Fisher and Callhome models, tested by inputting lattice oracle paths, 1-best paths, and full lattices.
['[EMPTY]', 'Lattice oracle', '1-best', 'Lattice']
To obtain a better understanding of the proposed model, we compare accuracies to the sequential self-attentional model when translating either lattice oracle paths, 1-best transcripts, or lattices. The lattice model translates sequences by treating them as lattices with only a single complete path and all transition probabilities set to 1. We can see that the lattice model outperforms the sequential model even when translating sequential 1-best transcripts, indicating benefits perhaps due to more robustness or increased training data size for the lattice model. However, the largest gains stem from using lattices at test time, indicating that our model is able to exploit the actual test-time lattices. Note that there is still a considerable gap to the translation of lattice oracles which form a top-line to our experiments.