@ -14,14 +14,33 @@ The belief state prediction task of SOLOIST utilizes *top-k* and *top-p* samplin
| **user:** we need to find a guesthouse of moderate price. <br/>**system:** do you have any special area you would like to stay?<br/>or possibly a star request for the guesthouse?<br/>**user:** i would like it to have a 3 star rating. | type = guesthouse<br/>pricerange = moderate<br/>stars = 3 | parking = yes<br/>stars = 3 |
| **user:** we need to find a guesthouse of moderate price. <br/>**system:** do you have any special area you would like to stay?<br/>or possibly a star request for the guesthouse?<br/>**user:** i would like it to have a 3 star rating. | type = guesthouse<br/>pricerange = moderate<br/>stars = 3 | parking = yes<br/>stars = 3 |
### Prompt-based Methods
### Prompt-based Methods
#### Value-based prompt
#### Value-based prompt & Inverse prompt
Value-based prompt utilizes the dialog history and value to generate corresponding slots. This approach doesn't rely on the ontology of the slots. While training, both value-based prompts and inverse prompts are used to compute the training loss. The inverse prompt mechanism helped complementing the value-based prompt in generating the correct slots. It's worth mentioning that there's a 5-10% drop (depending on the data split trained on) in the JGA score when inverse prompt mechanism is not applied during training.
The experimental results show a significant difference in the performance between baseline SOLOIST and Prompt-based methods. Prompt-based methods significantly outperformed the baseline model under low-resource settings (*5-dpd*, *10-dpd* and *50-dpd*).
#### destination vs departure & leave vs arrive
Under low-resource settings, the prompt-based model struggled while generate slots like *departure*|*destination* and *leave*|*arrive*. For many instances, it wrongly generated *destination* instead of *departure* and vice-versa. Below is one example where slots are wrongly generated.
#### destination vs departure
| Dialog History | True belief states | Generated belief states |
| **user:** I need to be picked up from pizza hut city centre after 04:30 | leave = 04:30<br/>departure = pizza hut city centre | arrive = 04:30<br/>destination = pizza hut city centre |
#### Duplicate values
#### Repeated values
Consider the following example:
| Dialog History | True belief states |
| ----- | ----- |
| **user:** hi, can you help me find a 3 star place to stay?<br/>**system:** Is there a particular area or price range you would like?<br/>**user:** how about a place in the centre of town that is of type hotel<br/>**system:** how long would you like to stay, and how many are in your party?<br/>**user:** I'll be arriving saturday and staying for 3 nights. there are 3 of us.| area = centre<br/>stars = 3<br/>type = hotel<br/>day = saturday<br/>people = 3<br/>stay = 3|
The repeated value `3` in the above example can lead to ambiguity for value-based prompt while generating the slots.
#### Multi-prompt methods
#### Multi-prompt methods
### Value Extraction
#### JGA and JGA* Scores
Higher JGA* scores suggest the current methods of extracting value candidates need improvements.
