XL-NBT: A Cross-lingual Neural Belief Tracking Framework
Table 3: Ablation test for hyper-parameter α and τ on English-to-German XL-NBT-D.
['[ITALIC]  [BOLD] α [BOLD]  ablation ( [ITALIC]  [BOLD] τ fixed to 0.1) value', '[ITALIC]  [BOLD] α [BOLD]  ablation ( [ITALIC]  [BOLD] τ fixed to 0.1) Goal', '[ITALIC]  [BOLD] α [BOLD]  ablation ( [ITALIC]  [BOLD] τ fixed to 0.1) Request', '[ITALIC]  [BOLD] τ [BOLD]  ablation ( [ITALIC]  [BOLD] α fixed to 1) value', '[ITALIC]  [BOLD] τ [BOLD]  ablation ( [ITALIC]  [BOLD] α fixed to 1) Goal', '[ITALIC]  [BOLD] τ [BOLD]  ablation ( [ITALIC]  [BOLD] α fixed to 1) Request']
a dramatic change of α will not harm the final results too much, we simply choose α=1 as the hyper-parameter. In contrast, the system is more sensitive to temperature. Too conservative replacement will lead to weak transfer, while too aggressive replacement will destroy the utterance representation. Therefore, we choose the a moderate temperature of τ=0.1 throughout our experiments. We also draw the learning curve (Precision vs. Iteration) in the Appendix for both XL-NBT-C and XL-NBT-D. The learning curves show that our algorithm is stable and converges quickly, and the reported results are highly reproducible.