qhjqhj00 klue-dst-eval
KLUE: Korean Language Understanding Evaluation — Sungjoon Park et al. (arXiv:2105.09680, 2021)
What this evaluates
Evaluates a model's ability to track and update dialogue state across turns in Korean conversations, testing multi-turn reasoning and slot filling.
Datasets
- KLUE-DST — total ?; splits: train (-1), val (-1), test (-1); repo https://github.com/KLUE-benchmark/KLUE
Metrics
Joint Accuracy(primary) — range: [0, 1]- Proportion of dialogue turns where all slot values are predicted exactly correctly.
Input / output format
Input: Dialogue history and current user utterance in Korean.
Output: Dictionary of slot-value pairs representing the current dialogue state.
Scoring recipe
def compute_joint_accuracy(pred_states, gold_states):
correct = sum(1 for p, g in zip(pred_states, gold_states) if p == g)
return correct / len(gold_states)
Common pitfalls
- Slot value normalization (e.g., numbers, dates) must match gold exactly.
- Error propagation across turns can unfairly penalize early mistakes.
Evidence (verbatim from paper)
KLUE introduces a comprehensive, ethically designed benchmark for Korean NLU with 8 tasks (Topic Classification, STS, NLI, NER, RE, DP, MRC, DST) built from scratch using diverse, copyright-respected corpora.
Citation
@misc{park2021klue,
title={KLUE: Korean Language Understanding Evaluation},
author={Sungjoon Park et al.},
year={2021},
note={arXiv:2105.09680}
}
- arXiv: 2105.09680