Semantic Textual Similarity
Semantic textual similarity deals with determining how similar two pieces of texts are. This can take the form of assigning a score from 1 to 5. Related tasks are paraphrase or duplicate identification.
Image source: Learning Semantic Textual Similarity from Conversations
Papers
Showing 1–10 of 2381 papers
Benchmark Results
| # | Model | Metric | Claimed | Verified | Status |
|---|---|---|---|---|---|
| 1 | SMARTRoBERTa | Dev Pearson Correlation | 92.8 | — | Unverified |
| 2 | DeBERTa (large) | Accuracy | 92.5 | — | Unverified |
| 3 | SMART-BERT | Dev Pearson Correlation | 90 | — | Unverified |
| 4 | MT-DNN-SMART | Pearson Correlation | 0.93 | — | Unverified |
| 5 | StructBERTRoBERTa ensemble | Pearson Correlation | 0.93 | — | Unverified |
| 6 | Mnet-Sim | Pearson Correlation | 0.93 | — | Unverified |
| 7 | XLNet (single model) | Pearson Correlation | 0.93 | — | Unverified |
| 8 | ALBERT | Pearson Correlation | 0.93 | — | Unverified |
| 9 | T5-11B | Pearson Correlation | 0.93 | — | Unverified |
| 10 | RoBERTa | Pearson Correlation | 0.92 | — | Unverified |