SlowGuess/NAACL_2021_OCR huggingface dataset:
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A.1 Slot Type Informed Description Construction
As shown in Table 4, each slot type has one prefix for appending to the beginning of the description. We used three different templates to construct the slot description. For all the booking slots (e.g., book people), we use "[prefix] [slot] for the [domain] booking". For boolean slots, we use "[prefix] [slot] in the [domain]". And for all the others, we use "[prefix] [slot] of the [domain]". When a slot name (e.g., train-day) overlap with the slot type (e.g., day) or a slot does not fall into any slot type category (others), we simply set the prefix as an empty string.
A.2 Full Shot Results
To understand the full shot performance of our T5DST model and whether slot description is still helpful when there is enough training data, we also conduct the experiments in a full data setting. As shown in Table 5, using slot description only improves the joint goal accuracy by  $0.56\%$  in MultiWoz 2.0 and  $0.30\%$  in MultiWoz 2.1, which indicates that the description is less effective when there is a large amount of data for training.
Compared to prior models with zero-shot capability, T5DST shows promising performance. Compared to other state-of-the-art models that optimized for full shot training, our model achieve competitive results in MultiWoz 2.0, but inferior results on MultiWoz 2.1. We notice that there are many training strategies (e.g., token masking (Kim et al., 2019; Heck et al., 2020)), additional supervision (e.g., full ontology (Chen et al., 2020)), and label cleaning strategies (Heck et al., 2020)) that may impact final full-shot result. We also expect higher performance with a larger T5 model, such as T5-base or T5-large. However, achieving SOTA in full-scale training is out of the scope of this work.
Slot Type	Slot Name	Prefix	Examples
Number	hotel-book stay, hotel-book people, hotel-stars, train-book people, restaurant-book people	number of	number of people for the hotel booking
Location	train-destination, train-departure, taxi-destination, taxi-departure	location of	location of destination of the train
Time	train-arriveby, train-leaveat, taxi-leaveat, restaurant-book time, taxi-arriveby	time of	time of arrive by of the train
Boolean	hotel-parking, hotel-internet	whether have	whether have parking in the hotel
Name	attraction-name, restaurant-name, hotel-name	-	name of attraction
Day	hotel-book day, train-day, restaurant-book day	-	day for the hotel booking
Others	hotel-type, attraction-type, hotel-area, attraction-area, restaurant-food, restaurant-pricerange, restaurant-area	-	type of the hotel
Table 4: Slot Type description examples. We define one prefix for each slot type. The prefix is empty when a slot name overlaps with the slot type or a slot does not fall into any slot type category (others).
Models	#Parameter	Zero-shot Inference	Joint Goal Accuracy
MWoz 2.0	MWoz 2.1
TRADE (Wu et al., 2019)	-	✓	48.62	45.6
STARC (Gao et al., 2020)	110M	✓	-	49.48
SUMBT (Lee et al., 2019)	✓	49.06	-
SGD-baseline (Rastogi et al., 2020)	110M	✓	-	43.4
T5DST	60M	✓	52.86	51.91
T5DST + Slot Type	60M	✓	53.42	52.21
DSTQA w/o span (Zhou and Small, 2019)	-	✗	51.44	51.17
MinTL (BART) (Lin et al., 2020)	400M	✗	52.10	53.67
SOM-DST (Kim et al., 2019)	340M	✗	52.32	53.68
SST (Chen et al., 2020)	110M	✗	51.17	55.23
TripPy (Heck et al., 2020)	110M	✗	-	55.29
SimpleTOD (Hosseini-Asl et al., 2020)	110M	✗	-	55.76
Table 5: Full shot results on MultiWOZ 2.0 and MultiWOZ 2.1.