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 | MT-DNN-SMART | Accuracy | 93.7 | — | Unverified |
| 2 | ALBERT | Accuracy | 93.4 | — | Unverified |
| 3 | RoBERTa (ensemble) | Accuracy | 92.3 | — | Unverified |
| 4 | BigBird | F1 | 91.5 | — | Unverified |
| 5 | StructBERTRoBERTa ensemble | Accuracy | 91.5 | — | Unverified |
| 6 | FLOATER-large | Accuracy | 91.4 | — | Unverified |
| 7 | SMART | Accuracy | 91.3 | — | Unverified |
| 8 | RoBERTa-large 355M + Entailment as Few-shot Learner | F1 | 91 | — | Unverified |
| 9 | RoBERTa-large 355M (MLP quantized vector-wise, fine-tuned) | Accuracy | 91 | — | Unverified |
| 10 | SpanBERT | Accuracy | 90.9 | — | Unverified |