End-to-End Slot Alignment and Recognition for Cross-Lingual NLU
Table 2: Results on MultiATIS++ using the full training data averaged over 5 runs. The Target only models are trained only on the target language training data. The Cross-lingual models are trained on the concatenation of training data in all languages.
['[BOLD] Intent acc. Target only', '[BOLD] Intent acc. LSTM', 'en 96.08', 'es 93.04', 'de 94.02', 'zh 92.50', 'ja 91.18', 'pt 92.70', 'fr 94.71', 'hi 84.46', 'tr 81.12']
The largest improvements are on the two low-resource languages – Hindi and Turkish. In addition, cross-lingual training with biLSTM on all the languages brings significant improvements on Hindi and Turkish over the target-only models – it improves intent accuracy by 2–5% and slot F1 by 4–11%. Further, BERT boosts the performance of cross-lingual training by 1–7% on intent accuracy and 1–9% on slot F1.