schema-guided-dstc8-eval
The SPPD System for Schema Guided Dialogue State Tracking Challenge — Li et al. (2020) (arXiv:2006.09035, 2020)
What this evaluates
Evaluates zero-shot dialogue state tracking across single and multi-domain conversations. It measures the model's ability to predict intents, extract slot values, and maintain accurate dialogue states over long contexts without prior exposure to unseen service domains.
Datasets
- Schema-Guided Dialogue (DSTC8 Track 4) — total ?; splits: train (-1), dev (-1)
Metrics
Joint Goal Accuracy(primary) — range: percent- Exact match rate of all slot values across all domains in a dialogue turn.
Average Goal Accuracy— range: percent- Mean of exact match rates computed per domain.
Intent Accuracy— range: percent- Percentage of correctly predicted dialogue intents.
Requested Slot F1— range: percent- F1 score for correctly identifying requested slots.
Input / output format
Input: Lower-cased user utterances, system dialogue actions, and service schema definitions encoded via BERT.
Output: Predicted intent label, slot values for each domain, and requested slots.
Scoring recipe
def joint_goal_accuracy(predictions, golds):
correct = 0
for pred, gold in zip(predictions, golds):
if pred == gold: # Exact match of all slot values across domains
correct += 1
return correct / len(predictions)
Common pitfalls
- Evaluating only on seen domains ignores the zero-shot capability explicitly tested on unseen services.
- Using original system utterances instead of dialogue actions degrades performance, as the authors found actions yield better results.
- Joint goal accuracy requires exact match across all domains; partial matches or domain-wise averaging will overstate performance.
Evidence (verbatim from paper)
Table 2: The overall performance of our system
Citation
@misc{li2020sppd,
title={The SPPD System for Schema Guided Dialogue State Tracking Challenge},
author={Li et al. (2020)},
year={2020},
note={arXiv:2006.09035}
}
- arXiv: 2006.09035