SlowGuess/ACL_2020_OCR huggingface dataset:
B Acc. per Slot on MultiWOZ 2.1 Testset
Domain-Slot	Frequency	Our Model without adaptive objective	Our Model	Δ
taxi-arrive by	1794	99.13	99.25	0.13
taxi-leave at	2165	99.14	99.27	0.13
taxi-departure	4037	98.12	98.37	0.25
taxi-destination	4108	98.1	98.26	0.17
attraction-name	5843	94.16	94.18	0.02
train-book people	6178	97.72	97.76	0.05
restaurant-name	7293	93.67	93.78	0.11
train-arrive by	7488	97.97	97.99	0.02
train-leave at	7563	96.05	96.22	0.16
hotel-internet	8012	97.26	97.16	-0.09
hotel-parking	8179	97.28	97.14	-0.13
hotel-name	8621	95.41	95.52	0.11
hotel-book stay	8715	99.44	99.46	0.01
hotel-book people	8734	99.35	99.28	-0.07
hotel-book day	8745	99.28	99.28	0
restaurant-book time	8958	99.15	99.3	0.16
restaurant-book day	9021	99.31	99.35	0.04
restaurant-book people	9026	99.35	99.35	0
hotel-stars	9330	98.31	98.41	0.1
attraction-area	9766	98.03	98.03	0
hotel-price range	9793	98.69	98.6	-0.09
hotel-type	10110	7.62	4.02	0.41
attraction-type	10525	7.39	0.12
hotel-area	10885	97.	97.67	0.15
restaurant-price range	1470	97.66	97.84	0.18
restaurant-area	1	97.68	97.86	0.19
train-day	15.	99.3	99.42	-0.01
train-departure	15672	98.4	98.48	0.06
train-destination	15951	98.63	98.7	0.07
restaurant-food	16095	97.54	97.61	0.06
Table 7. The detailed results of accuracy  $(\%)$  per slot before and after fine-tuning our model with adaptive objective on MultiWOZ 2.1 test set. We sort them in ascending order according to their frequency.  $\Delta$  means the differences of accuracy after fine-tuning.
6333