End-to-End Slot Alignment and Recognition for Cross-Lingual NLU
Table 4: Ablation results for zero-shot transfer learning averaged over 8 languages. Languages include es, de, zh, ja, pt, fr, hi, tr.
['[BOLD] Intent acc.', 'Average over 8 languages']
For intent accuracies, the components have no impact as the intent class is on the utterance level and alignment has no additional benefit. On the other hand, the soft-alignment components are crucial for slot labeling. Dropping the reconstruction loss results in a 3.6% drop, while dropping the source utterance is more severe and results in 8.4% degradation.