task_path stringlengths 3 199 ⌀ | dataset stringlengths 1 128 ⌀ | model_name stringlengths 1 223 ⌀ | paper_url stringlengths 21 601 ⌀ | metric_name stringlengths 1 50 ⌀ | metric_value stringlengths 1 9.22k ⌀ |
|---|---|---|---|---|---|
Data Integration > Table annotation > Column Type Annotation | GitTables-SemTab-DBP | DAGOBAH | https://www.eurecom.fr/fr/publication/6842 | F1 (%) | 7.00 |
Data Integration > Table annotation > Column Type Annotation | GitTables-SemTab-DBP | Kepler-aSI | https://www.semanticscholar.org/paper/Kepler-aSI-at-SemTab-2021-Baazouzi-Kachroudi/f4ef58ea481fc2dbcc57a97886150a9c33e17840 | F1 (%) | 4.1 |
Data Integration > Table annotation > Column Type Annotation | T2Dv2 | HNN + P2Vec | https://arxiv.org/abs/1906.00781v1 | Accuracy (%) | 96.6 |
Data Integration > Table annotation > Column Type Annotation | T2Dv2 | TURL | https://arxiv.org/abs/2006.14806v2 | Accuracy (%) | 96.2 |
Data Integration > Table annotation > Column Type Annotation | T2Dv2 | ColNet - Ensemble | http://arxiv.org/abs/1811.01304v2 | F1 (%) | 94.9 |
Data Integration > Table annotation > Column Type Annotation | WikiTables-TURL-CTA | TURL | https://arxiv.org/abs/2006.14806v2 | F1 (%) | 94.75 |
Data Integration > Table annotation > Column Type Annotation | WikiTables-TURL-CTA | DODUO | https://arxiv.org/abs/2104.01785v2 | F1 (%) | 92.45 |
Data Integration > Table annotation > Column Type Annotation | WikiTables-TURL-CTA | Watchog | https://dl.acm.org/doi/10.1145/3626766 | Macro-F1 | 78.72 |
Data Integration > Table annotation > Column Type Annotation | ToughTables-DBP | KGCODE-Tab | https://ceur-ws.org/Vol-3320/paper5.pdf | F1 (%) | 48 |
Data Integration > Table annotation > Column Type Annotation | ToughTables-DBP | JenTab | https://www.semanticscholar.org/paper/JenTab-Meets-SemTab-2021's-New-Challenges-Abdelmageed-Schindler/4f492fee6a7ae51d3f2527d9036a1beaf6f1e44b | F1 (%) | 46 |
Data Integration > Table annotation > Column Type Annotation | ToughTables-DBP | DAGOBAH | https://www.eurecom.fr/fr/publication/6842 | F1 (%) | 42.2 |
Data Integration > Table annotation > Column Type Annotation | ToughTables-DBP | Kepler-aSI | https://www.semanticscholar.org/paper/Kepler-aSI-at-SemTab-2021-Baazouzi-Kachroudi/f4ef58ea481fc2dbcc57a97886150a9c33e17840 | F1 (%) | 39.1 |
Data Integration > Table annotation > Column Type Annotation | ToughTables-DBP | s-elBat | https://www.semanticscholar.org/paper/Results-of-SemTab-2022-Abdelmageed-Chen/64dfbc1da6ad7402a6365c3e41667069a63599a6 | F1 (%) | 36.6 |
Data Integration > Table annotation > Column Type Annotation | ToughTables-DBP | MAGIC | https://www.semanticscholar.org/paper/MAGIC%3A-Mining-an-Augmented-Graph-using-INK%2C-from-a-Steenwinckel-Turck/03465d28e575ac8273887f0f56b67b890230d788 | F1 (%) | 15.9 |
Data Integration > Table annotation > Column Type Annotation | BiodivTab | KGCODE-Tab | https://ceur-ws.org/Vol-3320/paper5.pdf | F1 (%) | 86.7 |
Data Integration > Table annotation > Column Type Annotation | BiodivTab | TSOTSA | https://ceur-ws.org/Vol-3320/paper12.pdf | F1 (%) | 76 |
Data Integration > Table annotation > Column Type Annotation | BiodivTab | Kepler-aSI | https://www.semanticscholar.org/paper/Kepler-aSI-at-SemTab-2021-Baazouzi-Kachroudi/f4ef58ea481fc2dbcc57a97886150a9c33e17840 | F1 (%) | 59.3 |
Data Integration > Table annotation > Column Type Annotation | BiodivTab | DAGOBAH | https://www.eurecom.fr/fr/publication/6842 | F1 (%) | 34.4 |
Data Integration > Table annotation > Column Type Annotation | BiodivTab | MAGIC | https://www.semanticscholar.org/paper/MAGIC%3A-Mining-an-Augmented-Graph-using-INK%2C-from-a-Steenwinckel-Turck/03465d28e575ac8273887f0f56b67b890230d788 | F1 (%) | 14.2 |
Data Integration > Table annotation > Column Type Annotation | BiodivTab | JenTab | https://www.semanticscholar.org/paper/JenTab-Meets-SemTab-2021's-New-Challenges-Abdelmageed-Schindler/4f492fee6a7ae51d3f2527d9036a1beaf6f1e44b | F1 (%) | 10.7 |
Data Integration > Table annotation > Column Type Annotation | ToughTables-WD | DAGOBAH | https://www.eurecom.fr/fr/publication/6842 | F1 (%) | 83.2 |
Data Integration > Table annotation > Column Type Annotation | ToughTables-WD | Kepler-aSI | https://www.semanticscholar.org/paper/Kepler-aSI-at-SemTab-2021-Baazouzi-Kachroudi/f4ef58ea481fc2dbcc57a97886150a9c33e17840 | F1 (%) | 74.6 |
Data Integration > Table annotation > Column Type Annotation | ToughTables-WD | JenTab | https://www.semanticscholar.org/paper/JenTab-Meets-SemTab-2021's-New-Challenges-Abdelmageed-Schindler/4f492fee6a7ae51d3f2527d9036a1beaf6f1e44b | F1 (%) | 69.7 |
Data Integration > Table annotation > Column Type Annotation | ToughTables-WD | KGCODE-Tab | https://ceur-ws.org/Vol-3320/paper5.pdf | F1 (%) | 54.3 |
Data Integration > Table annotation > Column Type Annotation | ToughTables-WD | DAGOBAH | https://ceur-ws.org/Vol-3320/paper6.pdf | F1 (%) | 40.9 |
Data Integration > Table annotation > Column Type Annotation | VizNet-Sato-MultiColumn | DODUO | https://arxiv.org/abs/2104.01785v2 | Macro-F1 | 83.8 |
Data Integration > Table annotation > Column Type Annotation | VizNet-Sato-MultiColumn | Sato | https://arxiv.org/abs/1911.06311v3 | Weighted-F1 | 92.5 |
Data Integration > Table annotation > Column Type Annotation | VizNet-Sato-MultiColumn | Sato | https://arxiv.org/abs/1911.06311v3 | Macro-F1 | 73.5 |
Data Integration > Table annotation > Column Type Annotation | WDC SOTAB V2 | TorchicTab | https://www.csd.uoc.gr/~vefthym/SemTab2023/paper2.pdf | Micro F1 | 89.66 |
Data Integration > Table annotation > Column Type Annotation | WDC SOTAB V2 | gpt-3.5-turbo-0301-two-step | https://arxiv.org/abs/2306.00745v2 | Micro F1 | 89.47 |
Data Integration > Table annotation > Column Type Annotation | WDC SOTAB V2 | DREIFLUSS | https://ceur-ws.org/Vol-3557/paper4.pdf | Micro F1 | 38.04 |
Data Integration > Table annotation > Column Type Annotation | WDC SOTAB V2 | TSOTSA | https://ceur-ws.org/Vol-3557/paper6.pdf | Micro F1 | 37.05 |
Data Integration > Table annotation > Column Type Annotation | WDC SOTAB V2 | MUT2KG | https://ceur-ws.org/Vol-3557/paper5.pdf | Micro F1 | 32.01 |
Data Integration > Table annotation > Column Type Annotation | WikipediaGS-CTA | TURL | https://arxiv.org/abs/2006.14806v2 | Accuracy (%) | 74.6 |
Data Integration > Table annotation > Column Type Annotation | WikipediaGS-CTA | HNN | https://arxiv.org/abs/1906.00781v1 | Accuracy (%) | 65.5 |
Data Integration > Table annotation > Column Type Annotation | GitTables-SemTab-SCH | KGCODE-Tab | https://ceur-ws.org/Vol-3320/paper5.pdf | F1 (%) | 69.3 |
Data Integration > Table annotation > Column Type Annotation | GitTables-SemTab-SCH | DAGOBAH | https://www.eurecom.fr/fr/publication/6842 | F1 (%) | 18.3 |
Data Integration > Table annotation > Cell Entity Annotation | WikiTables-TURL-CEA | TURL | https://arxiv.org/abs/2006.14806v2 | F1 (%) | 68 |
Data Integration > Table annotation > Cell Entity Annotation | BiodivTab | KGCODE-Tab | https://ceur-ws.org/Vol-3320/paper5.pdf | F1 (%) | 91.1 |
Data Integration > Table annotation > Cell Entity Annotation | BiodivTab | TSOTSA | https://ceur-ws.org/Vol-3320/paper12.pdf | F1 (%) | 79 |
Data Integration > Table annotation > Cell Entity Annotation | BiodivTab | DAGOBAH | https://www.eurecom.fr/fr/publication/6842 | F1 (%) | 62 |
Data Integration > Table annotation > Cell Entity Annotation | BiodivTab | JenTab | https://www.semanticscholar.org/paper/JenTab-Meets-SemTab-2021's-New-Challenges-Abdelmageed-Schindler/4f492fee6a7ae51d3f2527d9036a1beaf6f1e44b | F1 (%) | 60.2 |
Data Integration > Table annotation > Cell Entity Annotation | BiodivTab | MAGIC | https://www.semanticscholar.org/paper/MAGIC%3A-Mining-an-Augmented-Graph-using-INK%2C-from-a-Steenwinckel-Turck/03465d28e575ac8273887f0f56b67b890230d788 | F1 (%) | 10 |
Data Integration > Table annotation > Cell Entity Annotation | WikipediaGS | TURL | https://arxiv.org/abs/2006.14806v2 | F1 (%) | 67 |
Data Integration > Table annotation > Cell Entity Annotation | ToughTables-WD | DAGOBAH | https://ceur-ws.org/Vol-3320/paper6.pdf | F1 (%) | 94.5 |
Data Integration > Table annotation > Cell Entity Annotation | ToughTables-WD | s-elBat | https://www.semanticscholar.org/paper/Results-of-SemTab-2022-Abdelmageed-Chen/64dfbc1da6ad7402a6365c3e41667069a63599a6 | F1 (%) | 93.8 |
Data Integration > Table annotation > Cell Entity Annotation | ToughTables-WD | DAGOBAH | https://www.eurecom.fr/fr/publication/6842 | F1 (%) | 92.3 |
Data Integration > Table annotation > Cell Entity Annotation | ToughTables-WD | Kepler-aSI | https://www.semanticscholar.org/paper/Kepler-aSI-at-SemTab-2021-Baazouzi-Kachroudi/f4ef58ea481fc2dbcc57a97886150a9c33e17840 | F1 (%) | 62 |
Data Integration > Table annotation > Cell Entity Annotation | ToughTables-WD | JenTab | https://www.semanticscholar.org/paper/JenTab-Meets-SemTab-2021's-New-Challenges-Abdelmageed-Schindler/4f492fee6a7ae51d3f2527d9036a1beaf6f1e44b | F1 (%) | 45.7 |
Data Integration > Table annotation > Cell Entity Annotation | ToughTables-DBP | DAGOBAH | https://www.eurecom.fr/fr/publication/6842 | F1 (%) | 94.5 |
Data Integration > Table annotation > Cell Entity Annotation | ToughTables-DBP | KGCODE-Tab | https://ceur-ws.org/Vol-3320/paper5.pdf | F1 (%) | 82.7 |
Data Integration > Table annotation > Cell Entity Annotation | ToughTables-DBP | JenTab | https://www.semanticscholar.org/paper/JenTab-Meets-SemTab-2021's-New-Challenges-Abdelmageed-Schindler/4f492fee6a7ae51d3f2527d9036a1beaf6f1e44b | F1 (%) | 60.7 |
Data Integration > Table annotation > Cell Entity Annotation | ToughTables-DBP | Kepler-aSI | https://www.semanticscholar.org/paper/Kepler-aSI-at-SemTab-2021-Baazouzi-Kachroudi/f4ef58ea481fc2dbcc57a97886150a9c33e17840 | F1 (%) | 50.9 |
Data Integration > Table annotation > Cell Entity Annotation | ToughTables-DBP | MAGIC | https://www.semanticscholar.org/paper/MAGIC%3A-Mining-an-Augmented-Graph-using-INK%2C-from-a-Steenwinckel-Turck/03465d28e575ac8273887f0f56b67b890230d788 | F1 (%) | 18.4 |
Data Integration > Table annotation > Columns Property Annotation | WDC SOTAB V2 | TorchicTab | https://www.csd.uoc.gr/~vefthym/SemTab2023/paper2.pdf | Micro F1 | 87.11 |
Data Integration > Table annotation > Columns Property Annotation | WDC SOTAB V2 | MUT2KG | https://ceur-ws.org/Vol-3557/paper5.pdf | Micro F1 | 79.35 |
Data Integration > Table annotation > Columns Property Annotation | WDC SOTAB V2 | TSOTSA | https://ceur-ws.org/Vol-3557/paper6.pdf | Micro F1 | 23.55 |
Data Integration > Table annotation > Columns Property Annotation | WDC SOTAB V2 | DREIFLUSS | https://ceur-ws.org/Vol-3557/paper4.pdf | Micro F1 | 17.39 |
Data Integration > Table annotation > Columns Property Annotation | WikiTables-TURL-CPA | TURL | https://arxiv.org/abs/2006.14806v2 | F1 (%) | 94.91 |
Data Integration > Table annotation > Columns Property Annotation | WikiTables-TURL-CPA | DODUO | https://arxiv.org/abs/2104.01785v2 | F1 (%) | 91.72 |
Data Integration > Table annotation > Columns Property Annotation | WikiTables-TURL-CPA | Watchog | https://dl.acm.org/doi/10.1145/3626766 | Macro-F1 | 88.45 |
Data Integration > Table annotation > Columns Property Annotation | WDC SOTAB | DODUO | https://ceur-ws.org/Vol-3320/paper1.pdf | Micro F1 | 79.96 |
Data Integration > Table annotation > Columns Property Annotation | WDC SOTAB | TURL | https://ceur-ws.org/Vol-3320/paper1.pdf | Micro F1 | 72.93 |
Data Integration > Table annotation > Columns Property Annotation | T2Dv2 | T2K | https://www.semanticscholar.org/paper/Matching-Web-Tables-To-DBpedia-A-Feature-Utility-Ritze-Bizer/74c2c4dc375515a2dc6e3d73993c3ad2d77b0757 | F1 (%) | 81 |
Data Integration > Table annotation > Table Type Detection | T2Dv2 | T2K | https://www.semanticscholar.org/paper/Matching-Web-Tables-To-DBpedia-A-Feature-Utility-Ritze-Bizer/74c2c4dc375515a2dc6e3d73993c3ad2d77b0757 | F1 (%) | 92 |
Data Integration > Table annotation > Row Annotation | T2Dv2 | T2K | https://www.semanticscholar.org/paper/Matching-Web-Tables-To-DBpedia-A-Feature-Utility-Ritze-Bizer/74c2c4dc375515a2dc6e3d73993c3ad2d77b0757 | F1 (%) | 80 |
Dialogue | Persona-Chat | BART (TextBox 2.0) | https://arxiv.org/abs/2212.13005v1 | BLEU-1 | 49.581 |
Dialogue | Persona-Chat | BART (TextBox 2.0) | https://arxiv.org/abs/2212.13005v1 | BLEU-2 | 39.24 |
Dialogue | Persona-Chat | BART (TextBox 2.0) | https://arxiv.org/abs/2212.13005v1 | Distinct-1 | 1.44 |
Dialogue | Persona-Chat | BART (TextBox 2.0) | https://arxiv.org/abs/2212.13005v1 | Distinct-2 | 8.89 |
Dialogue > Dialogue Generation | Ubuntu Dialogue (Activity) | MrRNN Act.-Ent. | http://arxiv.org/abs/1606.00776v2 | Precision | 16.84 |
Dialogue > Dialogue Generation | Ubuntu Dialogue (Activity) | MrRNN Act.-Ent. | http://arxiv.org/abs/1606.00776v2 | Recall | 9.72 |
Dialogue > Dialogue Generation | Ubuntu Dialogue (Activity) | MrRNN Act.-Ent. | http://arxiv.org/abs/1606.00776v2 | F1 | 11.43 |
Dialogue > Dialogue Generation | Harry Potter Dialogue Dataset | EVA | https://arxiv.org/abs/2211.06869v4 | mauve | 0.968 |
Dialogue > Dialogue Generation | Harry Potter Dialogue Dataset | Per-BOB | https://arxiv.org/abs/2211.06869v4 | mauve | 0.948 |
Dialogue > Dialogue Generation | Reddit (multi-ref) | SpaceFusion | http://arxiv.org/abs/1902.11205v3 | relevance (human) | 2.72 |
Dialogue > Dialogue Generation | Reddit (multi-ref) | SpaceFusion | http://arxiv.org/abs/1902.11205v3 | interest (human) | 2.53 |
Dialogue > Dialogue Generation | PG-19 | ∞-former (Sticky memories + initialized GPT-2 Small) | https://arxiv.org/abs/2109.00301v3 | Perplexity | 32.48 |
Dialogue > Dialogue Generation | Twitter Dialogue (Tense) | MrRNN Act.-Ent. | http://arxiv.org/abs/1606.00776v2 | Accuracy | 34.48% |
Dialogue > Dialogue Generation | Ubuntu Dialogue (Tense) | MrRNN Act.-Ent. | http://arxiv.org/abs/1606.00776v2 | Accuracy | 29.01% |
Dialogue > Dialogue Generation | Ubuntu Dialogue (Entity) | MrRNN Act.-Ent. | http://arxiv.org/abs/1606.00776v2 | Precision | 4.91 |
Dialogue > Dialogue Generation | Ubuntu Dialogue (Entity) | MrRNN Act.-Ent. | http://arxiv.org/abs/1606.00776v2 | Recall | 3.36 |
Dialogue > Dialogue Generation | Ubuntu Dialogue (Entity) | MrRNN Act.-Ent. | http://arxiv.org/abs/1606.00776v2 | F1 | 3.72 |
Dialogue > Dialogue Generation | Twitter Dialogue (Noun) | MrRNN Act.-Ent. | http://arxiv.org/abs/1606.00776v2 | Precision | 4.82 |
Dialogue > Dialogue Generation | Twitter Dialogue (Noun) | MrRNN Act.-Ent. | http://arxiv.org/abs/1606.00776v2 | Recall | 5.22 |
Dialogue > Dialogue Generation | Twitter Dialogue (Noun) | MrRNN Act.-Ent. | http://arxiv.org/abs/1606.00776v2 | F1 | 4.63 |
Dialogue > Dialogue Generation | FusedChat | Classification-based model | https://arxiv.org/abs/2109.04137v3 | Slot Accuracy | 0.973 |
Dialogue > Dialogue Generation | FusedChat | Classification-based model | https://arxiv.org/abs/2109.04137v3 | Joint SA | 0.600 |
Dialogue > Dialogue Generation | FusedChat | Classification-based model | https://arxiv.org/abs/2109.04137v3 | Inform | 75.1 |
Dialogue > Dialogue Generation | FusedChat | Classification-based model | https://arxiv.org/abs/2109.04137v3 | Inform_mct | 90.8 |
Dialogue > Dialogue Generation | FusedChat | Classification-based model | https://arxiv.org/abs/2109.04137v3 | Success | 60.9 |
Dialogue > Dialogue Generation | FusedChat | Classification-based model | https://arxiv.org/abs/2109.04137v3 | Success_mct | 74.4 |
Dialogue > Dialogue Generation | FusedChat | Classification-based model | https://arxiv.org/abs/2109.04137v3 | BLEU | 12.17 |
Dialogue > Dialogue Generation | FusedChat | Classification-based model | https://arxiv.org/abs/2109.04137v3 | PPL | 10.50 |
Dialogue > Dialogue Generation | FusedChat | Classification-based model | https://arxiv.org/abs/2109.04137v3 | Sensibleness | 0.58 |
Dialogue > Dialogue Generation | FusedChat | Classification-based model | https://arxiv.org/abs/2109.04137v3 | Specificity | 0.51 |
Dialogue > Dialogue Generation | FusedChat | Classification-based model | https://arxiv.org/abs/2109.04137v3 | SSA | 0.55 |
Dialogue > Dialogue Generation | FusedChat | Two-in-one model | https://arxiv.org/abs/2109.04137v3 | Slot Accuracy | 0.972 |
Dialogue > Dialogue Generation | FusedChat | Two-in-one model | https://arxiv.org/abs/2109.04137v3 | Joint SA | 0.592 |
Dialogue > Dialogue Generation | FusedChat | Two-in-one model | https://arxiv.org/abs/2109.04137v3 | Inform | 70.4 |
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