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47 lines
3.7 KiB
47 lines
3.7 KiB
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## Analysis of results and outputs
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### Baseline (SOLOIST)
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The baseline SOLOIST is fine-tuned on different data splits to evaluate the performance of belief state predictions task under low-resource settings. As the results show that the baseline SOLOIST model did perform well when *fine-tuned* on relatively large data samples, however, it performed poorly under low-resource training data (esp. 25 & 50 dialogs).
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The belief state prediction task of SOLOIST utilizes *top-k* and *top-p* sampling to generate the belief state slots and values. Since the baseline SOLOIST uses open-ended generation, it's susceptible to generating random slot-value pairs that are not relevant to the dialog history. Below is an example of how the baseline model generated a slot-value pair that's not relevant to user goals and it completely missed two correct slot-value pairs.
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| History | True belief states | Generated belief states |
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| ----- | ----- | ----- |
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| **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 |
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### Prompt-based Methods
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#### Value-based prompt & Inverse prompt
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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.
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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*).
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#### destination vs departure & leave vs arrive
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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.
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| Dialog History | True belief states | Generated belief states |
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|-------------------------------------------------------------------------| ----- | ----- |
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| **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 |
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#### Repeated values
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Consider the following example:
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| Dialog History | True belief states |
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| ----- | ----- |
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| **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|
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The repeated value `3` in the above example can lead to ambiguity for value-based prompt while generating the slots.
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#### Multi-prompt methods
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#### JGA and JGA* Scores
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Higher JGA* scores suggest the current methods of extracting value candidates need improvements.
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### Value Extraction
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