Domain Transfer in Dialogue Systems without Turn-Level Supervision
Table 2: Fine-tuning results for our pretrained baseline (bl) and the policy gradient (pg). The colored results along the left-to-right downward diagonal are in-domain results, dark red being the supervised results and light green the policy gradient fine-tuned results, and each pair of columns compare baseline and system results for each target domain. The Averages rows presents the average out-of-domain transfer scores for each domain. Note that while the PG method has access to more data, this does not invalidate the comparison, seeing that the additional data is relatively easy to obtain in an applied setting.
['PretrainFinetune', 'taxi bl', 'taxi pg', 'train bl', 'train pg', 'hotel bl', 'hotel pg', 'restaurant bl', 'restaurant pg', 'attraction bl', 'attraction pg']
We also report the average out-of-domain accuracies for each domain, to illustrate how policy gradient training in general performs compared to the baseline. The table show the performance of transferring from each domain to all other domains. From the results we observe that in almost all domain transfer settings, with the exception of restaurant to attraction, we get a consistent increase in performance when applying policy gradient fine-tuning, compared to the zero-shot transfer baselines. In some instances we also see an increase in performance from further fine-tuning a model after turn-level supervision convergence using only the dialogue-level reward feedback. In the case of attraction, we are even able to increase the accuracy by a large margin using in-domain policy gradient fine-tuning. On average, we see relative improvements of the accuracy, ranging from 0.03 to 0.2, when applying our proposed method of fine-tuning for DST domain transfer.