Intent Classification
Intent Classification is the task of correctly labeling a natural language utterance from a predetermined set of intents
Source: Multi-Layer Ensembling Techniques for Multilingual Intent Classification
Papers
Showing 11–20 of 344 papers
Benchmark Results
| # | Model | Metric | Claimed | Verified | Status |
|---|---|---|---|---|---|
| 1 | TDT 0-8 | Accuracy (%) | 90.07 | — | Unverified |
| 2 | Partially Fine-tuned HuBERT | Accuracy (%) | 87.51 | — | Unverified |
| 3 | Multi-SLURP | Accuracy (%) | 78.33 | — | Unverified |
| 4 | Finstreder (Conformer) | Accuracy (%) | 53.11 | — | Unverified |
| 5 | Finstreder (Quartznet) | Accuracy (%) | 43.15 | — | Unverified |
| # | Model | Metric | Claimed | Verified | Status |
|---|---|---|---|---|---|
| 1 | mT5 Base (encoder-only) | Intent Accuracy | 86.1 | — | Unverified |
| 2 | mT5 Base (text-to-text) | Intent Accuracy | 85.3 | — | Unverified |
| 3 | XLM-R Base | Intent Accuracy | 85.1 | — | Unverified |
| # | Model | Metric | Claimed | Verified | Status |
|---|---|---|---|---|---|
| 1 | RoBERTa-wwm-ext-base | Accuracy | 85.5 | — | Unverified |
| # | Model | Metric | Claimed | Verified | Status |
|---|---|---|---|---|---|
| 1 | BERT (query + URL) | F1-score | 0.77 | — | Unverified |