RSL19BD at DBDC4:Ensemble of Decision Tree-based and LSTM-based Models
Table 13: Official results on English data
['Model', 'Accuracy', 'F1 (B)', 'JSD (NB, PB, B)', 'MSE (NB, PB, B)']
It can be observed that Run 5 did well on average. For English runs, it outperformed all other runs in all evaluation metrics. For Japanese runs, it outperformed all other runs in JSD (NB, PB, B) and MSE (NB, PB, B). We analysed our runs in terms of MSE (NB, PB, B) (referred as MSE in this section), which is the emphasised evaluation metric in this challenge. To investigate how well the ensemble actually worked for each utterance, we would like to know the number of target system utterances for which the ensemble model outperformed the original models that were ensembled.