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 ⌀ |
|---|---|---|---|---|---|
Dialogue > Visual Dialog | Visual Dialog v1.0 test-std | adasd | null | R@5 | 77.53 |
Dialogue > Visual Dialog | Visual Dialog v1.0 test-std | adasd | null | R@10 | 87.9 |
Dialogue > Visual Dialog | Visual Dialog v1.0 test-std | adasd | null | Mean | 4.7 |
Dialogue > Visual Dialog | Visual Dialog v1.0 test-std | gat_disc_3 | null | NDCG (x 100) | 47.51 |
Dialogue > Visual Dialog | Visual Dialog v1.0 test-std | gat_disc_3 | null | MRR (x 100) | 53.19 |
Dialogue > Visual Dialog | Visual Dialog v1.0 test-std | gat_disc_3 | null | R@1 | 41.4 |
Dialogue > Visual Dialog | Visual Dialog v1.0 test-std | gat_disc_3 | null | R@5 | 65.85 |
Dialogue > Visual Dialog | Visual Dialog v1.0 test-std | gat_disc_3 | null | R@10 | 74.15 |
Dialogue > Visual Dialog | Visual Dialog v1.0 test-std | gat_disc_3 | null | Mean | 11.96 |
Dialogue > Visual Dialog | Visual Dialog v1.0 test-std | MN-QIH-D | http://arxiv.org/abs/1611.08669v5 | NDCG (x 100) | 47.5 |
Dialogue > Visual Dialog | Visual Dialog v1.0 test-std | MN-QIH-D | http://arxiv.org/abs/1611.08669v5 | MRR (x 100) | 55.5 |
Dialogue > Visual Dialog | Visual Dialog v1.0 test-std | MN-QIH-D | http://arxiv.org/abs/1611.08669v5 | R@1 | 40.98 |
Dialogue > Visual Dialog | Visual Dialog v1.0 test-std | MN-QIH-D | http://arxiv.org/abs/1611.08669v5 | R@5 | 72.30 |
Dialogue > Visual Dialog | Visual Dialog v1.0 test-std | MN-QIH-D | http://arxiv.org/abs/1611.08669v5 | R@10 | 83.30 |
Dialogue > Visual Dialog | Visual Dialog v1.0 test-std | MN-QIH-D | http://arxiv.org/abs/1611.08669v5 | Mean | 5.92 |
Dialogue > Visual Dialog | Visual Dialog v1.0 test-std | paratraining1epoch | null | NDCG (x 100) | 46.75 |
Dialogue > Visual Dialog | Visual Dialog v1.0 test-std | paratraining1epoch | null | MRR (x 100) | 53.3 |
Dialogue > Visual Dialog | Visual Dialog v1.0 test-std | paratraining1epoch | null | R@1 | 36.83 |
Dialogue > Visual Dialog | Visual Dialog v1.0 test-std | paratraining1epoch | null | R@5 | 73.45 |
Dialogue > Visual Dialog | Visual Dialog v1.0 test-std | paratraining1epoch | null | R@10 | 83.1 |
Dialogue > Visual Dialog | Visual Dialog v1.0 test-std | paratraining1epoch | null | Mean | 5.91 |
Dialogue > Visual Dialog | Visual Dialog v1.0 test-std | HRE-QIH-D | http://arxiv.org/abs/1611.08669v5 | NDCG (x 100) | 45.5 |
Dialogue > Visual Dialog | Visual Dialog v1.0 test-std | HRE-QIH-D | http://arxiv.org/abs/1611.08669v5 | MRR (x 100) | 54.2 |
Dialogue > Visual Dialog | Visual Dialog v1.0 test-std | HRE-QIH-D | http://arxiv.org/abs/1611.08669v5 | R@1 | 39.93 |
Dialogue > Visual Dialog | Visual Dialog v1.0 test-std | HRE-QIH-D | http://arxiv.org/abs/1611.08669v5 | R@5 | 70.45 |
Dialogue > Visual Dialog | Visual Dialog v1.0 test-std | HRE-QIH-D | http://arxiv.org/abs/1611.08669v5 | R@10 | 81.50 |
Dialogue > Visual Dialog | Visual Dialog v1.0 test-std | HRE-QIH-D | http://arxiv.org/abs/1611.08669v5 | Mean | 6.41 |
Dialogue > Visual Dialog | Visual Dialog v1.0 test-std | MN-QIH-D | http://arxiv.org/abs/1611.08669v5 | NDCG (x 100) | 45.3 |
Dialogue > Visual Dialog | Visual Dialog v1.0 test-std | MN-QIH-D | http://arxiv.org/abs/1611.08669v5 | MRR (x 100) | 55.4 |
Dialogue > Visual Dialog | Visual Dialog v1.0 test-std | MN-QIH-D | http://arxiv.org/abs/1611.08669v5 | R@1 | 40.95 |
Dialogue > Visual Dialog | Visual Dialog v1.0 test-std | MN-QIH-D | http://arxiv.org/abs/1611.08669v5 | R@5 | 72.45 |
Dialogue > Visual Dialog | Visual Dialog v1.0 test-std | MN-QIH-D | http://arxiv.org/abs/1611.08669v5 | R@10 | 82.83 |
Dialogue > Visual Dialog | Visual Dialog v1.0 test-std | MN-QIH-D | http://arxiv.org/abs/1611.08669v5 | Mean | 5.95 |
Dialogue > Visual Dialog | Visual Dialog v1.0 test-std | czczx | null | NDCG (x 100) | 23.0 |
Dialogue > Visual Dialog | Visual Dialog v1.0 test-std | czczx | null | MRR (x 100) | 29.97 |
Dialogue > Visual Dialog | Visual Dialog v1.0 test-std | czczx | null | R@1 | 16.62 |
Dialogue > Visual Dialog | Visual Dialog v1.0 test-std | czczx | null | R@5 | 43.58 |
Dialogue > Visual Dialog | Visual Dialog v1.0 test-std | czczx | null | R@10 | 53.05 |
Dialogue > Visual Dialog | Visual Dialog v1.0 test-std | czczx | null | Mean | 22.05 |
Dialogue > Visual Dialog | Visual Dialog v1.0 test-std | qqhe | null | NDCG (x 100) | 11.84 |
Dialogue > Visual Dialog | Visual Dialog v1.0 test-std | qqhe | null | MRR (x 100) | 7.25 |
Dialogue > Visual Dialog | Visual Dialog v1.0 test-std | qqhe | null | R@1 | 3.02 |
Dialogue > Visual Dialog | Visual Dialog v1.0 test-std | qqhe | null | R@5 | 7.22 |
Dialogue > Visual Dialog | Visual Dialog v1.0 test-std | qqhe | null | R@10 | 12.22 |
Dialogue > Visual Dialog | Visual Dialog v1.0 test-std | qqhe | null | Mean | 49.61 |
Dialogue > Dialogue Understanding > Spoken Language Understanding | Snips-SmartSpeaker | Finstreder (Conformer, character-based) | https://arxiv.org/abs/2206.14589v1 | Accuracy-EN (%) | 87.9 |
Dialogue > Dialogue Understanding > Spoken Language Understanding | Snips-SmartSpeaker | Finstreder (Conformer, character-based) | https://arxiv.org/abs/2206.14589v1 | Accuracy-FR (%) | 86.5 |
Dialogue > Dialogue Understanding > Spoken Language Understanding | Snips-SmartSpeaker | Finstreder (Conformer) | https://arxiv.org/abs/2206.14589v1 | Accuracy-EN (%) | 80.4 |
Dialogue > Dialogue Understanding > Spoken Language Understanding | Snips-SmartSpeaker | Finstreder (Conformer) | https://arxiv.org/abs/2206.14589v1 | Accuracy-FR (%) | 78.3 |
Dialogue > Dialogue Understanding > Spoken Language Understanding | Snips-SmartSpeaker | Finstreder (Quartznet) | https://arxiv.org/abs/2206.14589v1 | Accuracy-EN (%) | 77.6 |
Dialogue > Dialogue Understanding > Spoken Language Understanding | Snips-SmartSpeaker | Finstreder (Quartznet) | https://arxiv.org/abs/2206.14589v1 | Accuracy-FR (%) | 77.8 |
Dialogue > Dialogue Understanding > Spoken Language Understanding | Snips-SmartSpeaker | Snips | https://arxiv.org/abs/1810.12735v2 | Accuracy-EN (%) | 68.7 |
Dialogue > Dialogue Understanding > Spoken Language Understanding | Snips-SmartSpeaker | Snips | https://arxiv.org/abs/1810.12735v2 | Accuracy-FR (%) | 75.1 |
Dialogue > Dialogue Understanding > Spoken Language Understanding | Snips-SmartSpeaker | Google | https://arxiv.org/abs/1810.12735v2 | Accuracy-EN (%) | 47.8 |
Dialogue > Dialogue Understanding > Spoken Language Understanding | Snips-SmartSpeaker | Google | https://arxiv.org/abs/1810.12735v2 | Accuracy-FR (%) | 42.3 |
Dialogue > Dialogue Understanding > Spoken Language Understanding | Snips-SmartLights | Finstreder (Conformer, character-based) | https://arxiv.org/abs/2206.14589v1 | Accuracy (%) | 89.0 |
Dialogue > Dialogue Understanding > Spoken Language Understanding | Snips-SmartLights | Finstreder (Conformer) | https://arxiv.org/abs/2206.14589v1 | Accuracy (%) | 88.0 |
Dialogue > Dialogue Understanding > Spoken Language Understanding | Snips-SmartLights | AT-AT | https://arxiv.org/abs/2012.08549v1 | Accuracy (%) | 84.9 |
Dialogue > Dialogue Understanding > Spoken Language Understanding | Snips-SmartLights | Finstreder (Quartznet) | https://arxiv.org/abs/2206.14589v1 | Accuracy (%) | 84.8 |
Dialogue > Dialogue Understanding > Spoken Language Understanding | Snips-SmartLights | Snips | https://arxiv.org/abs/1810.12735v2 | Accuracy (%) | 84.2 |
Dialogue > Dialogue Understanding > Spoken Language Understanding | Snips-SmartLights | Google | https://arxiv.org/abs/1810.12735v2 | Accuracy (%) | 79.3 |
Dialogue > Dialogue Understanding > Spoken Language Understanding | Snips-SmartLights | Real + synthetic | https://arxiv.org/abs/1910.09463v1 | Accuracy (%) | 71.4 |
Dialogue > Dialogue Understanding > Spoken Language Understanding | Fluent Speech Commands | Finstreder (Conformer + AMT, character-based) | https://arxiv.org/abs/2206.14589v1 | Accuracy (%) | 99.8 |
Dialogue > Dialogue Understanding > Spoken Language Understanding | Fluent Speech Commands | UniverSLU | https://arxiv.org/abs/2310.02973v2 | Accuracy (%) | 99.8 |
Dialogue > Dialogue Understanding > Spoken Language Understanding | Fluent Speech Commands | Finstreder (Quartznet + AMT) | https://arxiv.org/abs/2206.14589v1 | Accuracy (%) | 99.7 |
Dialogue > Dialogue Understanding > Spoken Language Understanding | Fluent Speech Commands | textual-kd-slu | https://arxiv.org/abs/2010.13105v2 | Accuracy (%) | 99.7 |
Dialogue > Dialogue Understanding > Spoken Language Understanding | Fluent Speech Commands | Wav2Vec2.0-Classifier | https://arxiv.org/abs/2104.07253v2 | Accuracy (%) | 99.7 |
Dialogue > Dialogue Understanding > Spoken Language Understanding | Fluent Speech Commands | E2E SLP two-step | https://arxiv.org/abs/2102.06283v1 | Accuracy (%) | 99.7 |
Dialogue > Dialogue Understanding > Spoken Language Understanding | Fluent Speech Commands | Wav2vec 2.0 SSL | https://arxiv.org/abs/2111.14842v1 | Accuracy (%) | 99.6 |
Dialogue > Dialogue Understanding > Spoken Language Understanding | Fluent Speech Commands | Finstreder (Conformer) | https://arxiv.org/abs/2206.14589v1 | Accuracy (%) | 99.5 |
Dialogue > Dialogue Understanding > Spoken Language Understanding | Fluent Speech Commands | AT-AT | https://arxiv.org/abs/2012.08549v1 | Accuracy (%) | 99.5 |
Dialogue > Dialogue Understanding > Spoken Language Understanding | Fluent Speech Commands | BERT, AC Pretraining | https://arxiv.org/abs/2106.09009v2 | Accuracy (%) | 99.4 |
Dialogue > Dialogue Understanding > Spoken Language Understanding | Fluent Speech Commands | 3D-CNN+LSTM+CE | https://arxiv.org/abs/2106.04660v1 | Accuracy (%) | 99.3 |
Dialogue > Dialogue Understanding > Spoken Language Understanding | Fluent Speech Commands | Finstreder (Quartznet) | https://arxiv.org/abs/2206.14589v1 | Accuracy (%) | 99.2 |
Dialogue > Dialogue Understanding > Spoken Language Understanding | Fluent Speech Commands | Reptile | https://arxiv.org/abs/2008.01994v1 | Accuracy (%) | 99.2 |
Dialogue > Dialogue Understanding > Spoken Language Understanding | Fluent Speech Commands | FANS | https://arxiv.org/abs/2111.00400v1 | Accuracy (%) | 99.0 |
Dialogue > Dialogue Understanding > Spoken Language Understanding | Fluent Speech Commands | Pooling classifier pre-trained using force-aligned phoneme and word labels on LibriSpeech | https://arxiv.org/abs/1904.03670v2 | Accuracy (%) | 98.8 |
Dialogue > Dialogue Understanding > Spoken Language Understanding | Fluent Speech Commands | Amazon Alexa | https://arxiv.org/abs/2206.14589v1 | Accuracy (%) | 98.7 |
Dialogue > Dialogue Understanding > Spoken Language Understanding | Fluent Speech Commands | pGSLM+ | https://arxiv.org/abs/2303.00733v1 | Accuracy (%) | 98.2 |
Dialogue > Dialogue Understanding > Spoken Language Understanding | Timers and Such | Finstreder (Conformer) | https://arxiv.org/abs/2206.14589v1 | Accuracy (%) | 95.4 |
Dialogue > Dialogue Understanding > Spoken Language Understanding | Timers and Such | Finstreder (Quartznet) | https://arxiv.org/abs/2206.14589v1 | Accuracy (%) | 90.0 |
Dialogue > Dialogue Understanding > Spoken Language Understanding | Timers and Such | Baseline | https://arxiv.org/abs/2104.01604v2 | Accuracy (%) | 81.6 |
Dialogue > Dialogue Understanding > Spoken Language Understanding | Spoken-SQuAD | ALBERT | https://arxiv.org/abs/2204.14272v1 | F1 score | 77.1 |
Dialogue > Dialogue Understanding > Spoken Language Understanding | Spoken-SQuAD | SpeechBERT | https://arxiv.org/abs/1910.11559v4 | F1 score | 71.75 |
Dialogue > Dialogue Understanding > Spoken Language Understanding | Spoken-SQuAD | QANet + GAN | http://arxiv.org/abs/1904.07904v1 | F1 score | 63.11 |
Dialogue > Dialogue Understanding > Spoken Language Understanding | Spoken-SQuAD | Baseline | http://arxiv.org/abs/1804.00320v1 | F1 score | 58.71 |
Dialogue > Dialogue Understanding > Spoken Language Understanding > Spoken language identification | VOXLINGUA107 | Noisy | https://arxiv.org/pdf/2011.12998.pdf | 0..5sec | 12.3 |
Dialogue > Dialogue Understanding > Spoken Language Understanding > Spoken language identification | VOXLINGUA107 | Noisy | https://arxiv.org/pdf/2011.12998.pdf | 5..20sec | 6.1 |
Dialogue > Dialogue Understanding > Spoken Language Understanding > Spoken language identification | VOXLINGUA107 | Noisy | https://arxiv.org/pdf/2011.12998.pdf | Average | 7.1 |
Dialogue > Dialogue Understanding > Spoken Language Understanding > Spoken language identification | VOXLINGUA107 | Cleaned | https://arxiv.org/pdf/2011.12998.pdf | 0..5sec | 13.4 |
Dialogue > Dialogue Understanding > Spoken Language Understanding > Spoken language identification | VOXLINGUA107 | Cleaned | https://arxiv.org/pdf/2011.12998.pdf | 5..20sec | 6.6 |
Dialogue > Dialogue Understanding > Spoken Language Understanding > Spoken language identification | VOXLINGUA107 | Cleaned | https://arxiv.org/pdf/2011.12998.pdf | Average | 7.6 |
Dialogue > Dialogue Understanding > Spoken Language Understanding > Spoken language identification | YouTube News dataset (Background Music) | Inception-v3 CRNN | http://arxiv.org/abs/1708.04811v1 | F1 Score | 0.89 |
Dialogue > Dialogue Understanding > Spoken Language Understanding > Spoken language identification | YouTube News dataset (Background Music) | Inception-v3 CRNN | http://arxiv.org/abs/1708.04811v1 | Accuracy | 0.89 |
Dialogue > Dialogue Understanding > Spoken Language Understanding > Spoken language identification | YouTube News dataset (Background Music) | CRNN | http://arxiv.org/abs/1708.04811v1 | F1 Score | 0.70 |
Dialogue > Dialogue Understanding > Spoken Language Understanding > Spoken language identification | YouTube News dataset (Background Music) | CRNN | http://arxiv.org/abs/1708.04811v1 | Accuracy | 0.70 |
Dialogue > Dialogue Understanding > Spoken Language Understanding > Spoken language identification | YouTube News dataset (No Noise) | CRNN | https://arxiv.org/abs/2110.03427v3 | Accuracy | 0.967 |
Dialogue > Dialogue Understanding > Spoken Language Understanding > Spoken language identification | YouTube News dataset (No Noise) | CRNN Attention | https://arxiv.org/abs/2110.03427v3 | Accuracy | 0.966 |
Dialogue > Dialogue Understanding > Spoken Language Understanding > Spoken language identification | YouTube News dataset (No Noise) | Inception-v3 CRNN | http://arxiv.org/abs/1708.04811v1 | F1 Score | 0.96 |
Dialogue > Dialogue Understanding > Spoken Language Understanding > Spoken language identification | YouTube News dataset (No Noise) | Inception-v3 CRNN | http://arxiv.org/abs/1708.04811v1 | Accuracy | 0.96 |
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