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 ⌀ |
|---|---|---|---|---|---|
Machine Translation | Alexa Point of View | T5 | https://arxiv.org/abs/2010.02600v2 | BLEU | 63 |
Machine Translation | WMT 2017 Latvian-English | Transformer trained on highly filtered data | http://arxiv.org/abs/1810.08392v1 | BLEU | 24.37 |
Machine Translation | WMT 2017 Latvian-English | mLSTM with factored data | https://aclanthology.org/W17-4737 | BLEU | 20.80 |
Machine Translation | WMT 2017 Latvian-English | Attention-based Hybrid NMT combination | http://arxiv.org/abs/1710.03743v1 | BLEU | 14.83 |
Machine Translation | WMT 2017 Latvian-English | RNN | http://arxiv.org/abs/1808.02733v1 | BLEU | - |
Machine Translation | WMT2016 English-Russian | Attentional encoder-decoder + BPE | http://arxiv.org/abs/1606.02891v2 | BLEU score | 26.0 |
Machine Translation | WMT2016 English-Russian | PBSMT + NMT | http://arxiv.org/abs/1804.07755v2 | BLEU score | 13.76 |
Machine Translation | WMT2016 English-Russian | Unsupervised PBSMT | http://arxiv.org/abs/1804.07755v2 | BLEU score | 13.37 |
Machine Translation | WMT2016 English-Russian | Unsupervised NMT + Transformer | http://arxiv.org/abs/1804.07755v2 | BLEU score | 7.98 |
Machine Translation | Arba Sicula | Larger | https://arxiv.org/abs/2110.01938v1 | BLEU (En-Scn) | 35.0 |
Machine Translation | Arba Sicula | Larger | https://arxiv.org/abs/2110.01938v1 | BLEU (Scn-En) | 36.8 |
Machine Translation | Arba Sicula | Many-to-Many | https://arxiv.org/abs/2110.01938v1 | BLEU (It-Scn) | 36.5 |
Machine Translation | Arba Sicula | Many-to-Many | https://arxiv.org/abs/2110.01938v1 | BLEU (Scn-It) | 30.9 |
Machine Translation | WMT2014 English-Czech | Evolved Transformer Big | https://arxiv.org/abs/1901.11117v4 | BLEU score | 28.2 |
Machine Translation | WMT2014 English-Czech | Evolved Transformer Base | https://arxiv.org/abs/1901.11117v4 | BLEU score | 27.6 |
Machine Translation | WMT2017 Turkish-English | HeadMask (Random-18) | https://arxiv.org/abs/2009.09672v2 | BLEU score | 17.56 |
Machine Translation | WMT2017 Turkish-English | HeadMask (Impt-18) | https://arxiv.org/abs/2009.09672v2 | BLEU score | 17.48 |
Machine Translation | ACES | HWTSC-Teacher-Sim | https://arxiv.org/abs/2210.15615v2 | Score | 19.97 |
Machine Translation | ACES | MS-COMET-22 | https://arxiv.org/abs/2210.15615v2 | Score | 19.89 |
Machine Translation | ACES | MS-COMET-QE-22 | https://arxiv.org/abs/2210.15615v2 | Score | 19.76 |
Machine Translation | ACES | KG-BERTScore | https://arxiv.org/abs/2210.15615v2 | Score | 17.28 |
Machine Translation | ACES | metricx_xl_DA_2019 | https://arxiv.org/abs/2210.15615v2 | Score | 17.17 |
Machine Translation | ACES | COMET-QE | https://arxiv.org/abs/2210.15615v2 | Score | 16.8 |
Machine Translation | ACES | COMET-22 | https://arxiv.org/abs/2210.15615v2 | Score | 16.31 |
Machine Translation | ACES | UniTE-src | https://arxiv.org/abs/2210.15615v2 | Score | 15.68 |
Machine Translation | ACES | UniTE-ref | https://arxiv.org/abs/2210.15615v2 | Score | 15.38 |
Machine Translation | ACES | metricx_xxl_DA_2019 | https://arxiv.org/abs/2210.15615v2 | Score | 15.24 |
Machine Translation | ACES | UniTE | https://arxiv.org/abs/2210.15615v2 | Score | 14.76 |
Machine Translation | ACES | Cross-QE | https://arxiv.org/abs/2210.15615v2 | Score | 14.07 |
Machine Translation | ACES | chrF | https://arxiv.org/abs/2210.15615v2 | Score | 13.57 |
Machine Translation | ACES | metricx_xl_MQM_2020 | https://arxiv.org/abs/2210.15615v2 | Score | 13.08 |
Machine Translation | ACES | COMET-20 | https://arxiv.org/abs/2210.15615v2 | Score | 12.06 |
Machine Translation | ACES | BLEURT-20 | https://arxiv.org/abs/2210.15615v2 | Score | 11.9 |
Machine Translation | ACES | YiSi-1 | https://arxiv.org/abs/2210.15615v2 | Score | 11.38 |
Machine Translation | ACES | BERTScore | https://arxiv.org/abs/2210.15615v2 | Score | 10.47 |
Machine Translation | ACES | BLEU | https://arxiv.org/abs/2210.15615v2 | Score | -3.13 |
Machine Translation | ACES | f101spBLEU | https://arxiv.org/abs/2210.15615v2 | Score | -0.33 |
Machine Translation | ACES | f200spBLEU | https://arxiv.org/abs/2210.15615v2 | Score | -0.18 |
Machine Translation | IWSLT2014 German-English | PiNMT | https://arxiv.org/abs/2310.19680v4 | BLEU score | 40.43 |
Machine Translation | IWSLT2014 German-English | BiBERT | https://arxiv.org/abs/2109.04588v1 | BLEU score | 38.61 |
Machine Translation | IWSLT2014 German-English | BiBERT | https://arxiv.org/abs/2109.04588v1 | Number of Params | 73.8M |
Machine Translation | IWSLT2014 German-English | Bi-SimCut | https://arxiv.org/abs/2206.02368v2 | BLEU score | 38.37 |
Machine Translation | IWSLT2014 German-English | Cutoff + Relaxed Attention + LM | https://arxiv.org/abs/2209.09735v1 | BLEU score | 37.96 |
Machine Translation | IWSLT2014 German-English | Cutoff + Relaxed Attention + LM | https://arxiv.org/abs/2209.09735v1 | Number of Params | 24.1M |
Machine Translation | IWSLT2014 German-English | DRDA | https://arxiv.org/abs/2406.02517v1 | BLEU score | 37.95 |
Machine Translation | IWSLT2014 German-English | Transformer + R-Drop + Cutoff | https://arxiv.org/abs/2106.14448v2 | BLEU score | 37.90 |
Machine Translation | IWSLT2014 German-English | SimCut | https://arxiv.org/abs/2206.02368v2 | BLEU score | 37.81 |
Machine Translation | IWSLT2014 German-English | Cutoff+Knee | https://arxiv.org/abs/2003.03977v5 | BLEU score | 37.78 |
Machine Translation | IWSLT2014 German-English | Cutoff | https://arxiv.org/abs/2009.13818v2 | BLEU score | 37.6 |
Machine Translation | IWSLT2014 German-English | CipherDAug | https://arxiv.org/abs/2204.00665v1 | BLEU score | 37.53 |
Machine Translation | IWSLT2014 German-English | Transformer + R-Drop | https://arxiv.org/abs/2106.14448v2 | BLEU score | 37.25 |
Machine Translation | IWSLT2014 German-English | Data Diversification | https://arxiv.org/abs/1911.01986v4 | BLEU score | 37.2 |
Machine Translation | IWSLT2014 German-English | UniDrop | https://arxiv.org/abs/2104.04946v1 | BLEU score | 36.88 |
Machine Translation | IWSLT2014 German-English | MixedRepresentations | https://icml.cc/Conferences/2020/ScheduleMultitrack?event=6391 | BLEU score | 36.41 |
Machine Translation | IWSLT2014 German-English | Mask Attention Network (small) | https://arxiv.org/abs/2103.13597v1 | BLEU score | 36.3 |
Machine Translation | IWSLT2014 German-English | Mask Attention Network (small) | https://arxiv.org/abs/2103.13597v1 | Number of Params | 37M |
Machine Translation | IWSLT2014 German-English | MUSE(Parallel Multi-scale Attention) | https://arxiv.org/abs/1911.09483v1 | BLEU score | 36.3 |
Machine Translation | IWSLT2014 German-English | Transformer+Rep(Sim)+WDrop | https://arxiv.org/abs/2104.01853v1 | BLEU score | 36.22 |
Machine Translation | IWSLT2014 German-English | Transformer+Rep(Sim)+WDrop | https://arxiv.org/abs/2104.01853v1 | Number of Params | 37M |
Machine Translation | IWSLT2014 German-English | MAT | https://arxiv.org/abs/2006.10270v2 | BLEU score | 36.22 |
Machine Translation | IWSLT2014 German-English | TransformerBase + AutoDropout | https://arxiv.org/abs/2101.01761v1 | BLEU score | 35.8 |
Machine Translation | IWSLT2014 German-English | Local Joint Self-attention | https://arxiv.org/abs/1905.06596v1 | BLEU score | 35.7 |
Machine Translation | IWSLT2014 German-English | TaLK Convolutions | https://arxiv.org/abs/2002.03184v2 | BLEU score | 35.5 |
Machine Translation | IWSLT2014 German-English | ImitKD + Full | https://arxiv.org/abs/2009.07253v2 | BLEU score | 35.4 |
Machine Translation | IWSLT2014 German-English | DeLighT | https://arxiv.org/abs/2008.00623v2 | BLEU score | 35.3 |
Machine Translation | IWSLT2014 German-English | DynamicConv | http://arxiv.org/abs/1901.10430v2 | BLEU score | 35.2 |
Machine Translation | IWSLT2014 German-English | Transformer | https://arxiv.org/abs/2205.07260v1 | BLEU score | 35.1385 |
Machine Translation | IWSLT2014 German-English | LightConv | http://arxiv.org/abs/1901.10430v2 | BLEU score | 34.8 |
Machine Translation | IWSLT2014 German-English | Transformer | https://arxiv.org/abs/1706.03762v7 | BLEU score | 34.44 |
Machine Translation | IWSLT2014 German-English | Rfa-Gate-arccos | https://arxiv.org/abs/2103.02143v2 | BLEU score | 34.4 |
Machine Translation | IWSLT2014 German-English | Variational Attention | http://arxiv.org/abs/1807.03756v2 | BLEU score | 33.1 |
Machine Translation | IWSLT2014 German-English | Minimum Risk Training [Edunov2017] | http://arxiv.org/abs/1711.04956v5 | BLEU score | 32.84 |
Machine Translation | IWSLT2014 German-English | CNAT | https://arxiv.org/abs/2103.11405v1 | BLEU score | 31.15 |
Machine Translation | IWSLT2014 German-English | Neural PBMT + LM [Huang2018] | http://arxiv.org/abs/1706.05565v8 | BLEU score | 30.08 |
Machine Translation | IWSLT2014 German-English | Back-Translation Finetuning | https://arxiv.org/abs/1912.10514v3 | BLEU score | 28.83 |
Machine Translation | IWSLT2014 German-English | Actor-Critic [Bahdanau2017] | http://arxiv.org/abs/1607.07086v3 | BLEU score | 28.53 |
Machine Translation | V_C (trained on T_H) | M_C | https://arxiv.org/abs/2102.06320v1 | Median Relative Edit Distance | 0.27 |
Machine Translation | WMT2014 French-English | FLAN 137B (few-shot, k=9) | https://arxiv.org/abs/2109.01652v5 | BLEU score | 37.9 |
Machine Translation | WMT2014 French-English | FLAN 137B (zero-shot) | https://arxiv.org/abs/2109.01652v5 | BLEU score | 35.9 |
Machine Translation | WMT2014 French-English | SMT + iterative backtranslation (unsupervised) | http://arxiv.org/abs/1809.01272v1 | BLEU score | 25.87 |
Machine Translation | WMT2016 English-German | MADL | https://openreview.net/forum?id=HyGhN2A5tm | BLEU score | 40.68 |
Machine Translation | WMT2016 English-German | Attentional encoder-decoder + BPE | http://arxiv.org/abs/1606.02891v2 | BLEU score | 34.2 |
Machine Translation | WMT2016 English-German | Linguistic Input Features | http://arxiv.org/abs/1606.02892v2 | BLEU score | 28.4 |
Machine Translation | WMT2016 English-German | DeLighT | https://arxiv.org/abs/2008.00623v2 | BLEU score | 28.0 |
Machine Translation | WMT2016 English-German | FLAN 137B (zero-shot) | https://arxiv.org/abs/2109.01652v5 | BLEU score | 27.0 |
Machine Translation | WMT2016 English-German | Transformer | https://arxiv.org/abs/1910.04209v3 | BLEU score | 26.7 |
Machine Translation | WMT2016 English-German | FLAN 137B (few-shot, k=11) | https://arxiv.org/abs/2109.01652v5 | BLEU score | 26.1 |
Machine Translation | WMT2016 English-German | BiRNN + GCN (Syn + Sem) | https://arxiv.org/abs/1804.08313v2 | BLEU score | 24.9 |
Machine Translation | WMT2016 English-German | SMT + iterative backtranslation (unsupervised) | http://arxiv.org/abs/1809.01272v1 | BLEU score | 18.23 |
Machine Translation | WMT2016 English-German | Unsupervised NMT + weight-sharing | http://arxiv.org/abs/1804.09057v1 | BLEU score | 10.86 |
Machine Translation | WMT2016 English-German | Unsupervised S2S with attention | http://arxiv.org/abs/1711.00043v2 | BLEU score | 9.64 |
Machine Translation | WMT2016 English-German | Exploiting Mono at Scale (single) | https://aclanthology.org/D19-1430 | SacreBLEU | 40.9 |
Machine Translation | ACCURAT balanced test corpus for under resourced languages Russian-Estonian | Multilingual Transformer | https://aclanthology.org/L18-1595 | BLEU | 18.03 |
Machine Translation | WMT2017 Russian-English | OmniNetP | https://arxiv.org/abs/2103.01075v1 | BLEU | 36.2 |
Machine Translation | WMT2016 German-English | FLAN 137B (few-shot, k=11) | https://arxiv.org/abs/2109.01652v5 | BLEU score | 40.7 |
Machine Translation | WMT2016 German-English | FLAN 137B (zero-shot) | https://arxiv.org/abs/2109.01652v5 | BLEU score | 38.9 |
Machine Translation | WMT2016 German-English | Attentional encoder-decoder + BPE | http://arxiv.org/abs/1606.02891v2 | BLEU score | 38.6 |
Machine Translation | WMT2016 German-English | Linguistic Input Features | http://arxiv.org/abs/1606.02892v2 | BLEU score | 32.9 |
Machine Translation | WMT2016 German-English | SMT + iterative backtranslation (unsupervised) | http://arxiv.org/abs/1809.01272v1 | BLEU score | 23.05 |
Machine Translation | WMT2016 German-English | Unsupervised NMT + weight-sharing | http://arxiv.org/abs/1804.09057v1 | BLEU score | 14.62 |
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