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 | WMT2014 English-German | SMT + iterative backtranslation (unsupervised) | http://arxiv.org/abs/1809.01272v1 | Operations per network pass | null |
Machine Translation | WMT2014 English-German | Reverse RNN Enc-Dec | http://arxiv.org/abs/1508.04025v5 | BLEU score | 14.0 |
Machine Translation | WMT2014 English-German | Reverse RNN Enc-Dec | http://arxiv.org/abs/1508.04025v5 | Hardware Burden | null |
Machine Translation | WMT2014 English-German | Reverse RNN Enc-Dec | http://arxiv.org/abs/1508.04025v5 | Operations per network pass | null |
Machine Translation | WMT2014 English-German | RNN Enc-Dec | http://arxiv.org/abs/1508.04025v5 | BLEU score | 11.3 |
Machine Translation | WMT2014 English-German | RNN Enc-Dec | http://arxiv.org/abs/1508.04025v5 | Hardware Burden | null |
Machine Translation | WMT2014 English-German | RNN Enc-Dec | http://arxiv.org/abs/1508.04025v5 | Operations per network pass | null |
Machine Translation | WMT2014 English-German | MAT | https://arxiv.org/abs/2006.10270v2 | SacreBLEU | 29.9 |
Machine Translation | WMT 2022 English-German | Vega-MT | https://arxiv.org/abs/2209.09444v4 | SacreBLEU | 37.8 |
Machine Translation | slone/myv_ru_2022 ru-myv | slone/mbart-large-51-mul-myv-v1 | https://arxiv.org/abs/2209.09368v1 | ChrF++ | 41.16 |
Machine Translation | Business Scene Dialogue JA-EN | Transformer-base | https://arxiv.org/abs/2008.01940v1 | BLEU | 12.88 |
Machine Translation | WMT 2017 English-Chinese | DynamicConv | http://arxiv.org/abs/1901.10430v2 | BLEU score | 24.4 |
Machine Translation | WMT 2017 English-Chinese | LightConv | http://arxiv.org/abs/1901.10430v2 | BLEU score | 24.3 |
Machine Translation | WMT 2017 English-Chinese | Hassan et al. (2018) | http://arxiv.org/abs/1803.05567v2 | BLEU score | 24.2 |
Machine Translation | IWSLT2017 English-French | Transformer base + BPE-Dropout | https://arxiv.org/abs/1910.13267v2 | Cased sacreBLEU | 39.83 |
Machine Translation | IWSLT2017 English-French | NLLB-200 | https://arxiv.org/abs/2207.04672v3 | SacreBLEU | 43 |
Machine Translation | FLoRes-200 | GenTranslate-7B | https://arxiv.org/abs/2402.06894v2 | BLEU | 38.5 |
Machine Translation | FLoRes-200 | NLLB-3.3B | https://arxiv.org/abs/2207.04672v3 | BLEU | 37.5 |
Machine Translation | FLoRes-200 | SeamlessM4T-Large-V1 | https://arxiv.org/abs/2308.11596v3 | BLEU | 37.5 |
Machine Translation | FLoRes-200 | BigTranslate | https://arxiv.org/abs/2305.18098v3 | BLEU | 22.8 |
Machine Translation | FLoRes-200 | ALMA-13B | https://arxiv.org/abs/2309.11674v2 | BLEU | 18.0 |
Machine Translation | IWSLT2015 English-Vietnamese | EnViT5 + MTet | https://arxiv.org/abs/2210.05610v2 | BLEU | 40.2 |
Machine Translation | IWSLT2015 English-Vietnamese | Tall Transformer with Style-Augmented Training | https://blog.vietai.org/sat/ | BLEU | 37.8 |
Machine Translation | IWSLT2015 English-Vietnamese | Transformer+BPE-dropout | https://arxiv.org/abs/1910.13267v2 | BLEU | 33.27 |
Machine Translation | IWSLT2015 English-Vietnamese | Transformer+BPE+FixNorm+ScaleNorm | https://arxiv.org/abs/1910.05895v2 | BLEU | 32.8 |
Machine Translation | IWSLT2015 English-Vietnamese | Transformer+LayerNorm-simple | https://arxiv.org/abs/1911.07013v1 | BLEU | 31.4 |
Machine Translation | IWSLT2015 English-Vietnamese | CVT | http://arxiv.org/abs/1809.08370v1 | BLEU | 29.6 |
Machine Translation | IWSLT2015 English-Vietnamese | Self-Adaptive Control of Temperature | http://arxiv.org/abs/1808.07374v2 | BLEU | 29.12 |
Machine Translation | IWSLT2015 English-Vietnamese | SAWR | https://arxiv.org/abs/1905.02878v1 | BLEU | 29.09 |
Machine Translation | IWSLT2015 English-Vietnamese | DeconvDec | http://arxiv.org/abs/1806.03692v1 | BLEU | 28.47 |
Machine Translation | IWSLT2015 English-Vietnamese | LSTM+Attention+Ensemble | https://www.semanticscholar.org/paper/Stanford-Neural-Machine-Translation-Systems-for-Luong-Manning/2826f9dccdcceb113b33ccf2841d488f1419bb30 | BLEU | 26.4 |
Machine Translation | FRMT (Chinese - Taiwan) | PaLM 2 | https://arxiv.org/abs/2305.10403v3 | BLEURT | 72.0 |
Machine Translation | FRMT (Chinese - Taiwan) | PaLM | https://arxiv.org/abs/2305.10403v3 | BLEURT | 68.6 |
Machine Translation | FRMT (Chinese - Taiwan) | Google Translate | https://arxiv.org/abs/2305.10403v3 | BLEURT | 68.5 |
Machine Translation | WMT2017 Chinese-English | StrokeNet | https://arxiv.org/abs/2211.12781v1 | BLEU | 26.5 |
Machine Translation | WMT2017 Chinese-English | T2R + Pretrain | https://arxiv.org/abs/2103.13076v2 | BLEU | 23.8 |
Machine Translation | WMT2017 Chinese-English | OmniNetP | https://arxiv.org/abs/2103.01075v1 | BLEU | 23.0 |
Machine Translation | WMT2016 English-Romanian | DeLighT | https://arxiv.org/abs/2008.00623v2 | BLEU score | 34.7 |
Machine Translation | WMT2016 English-Romanian | CMLM+LAT+4 iterations | https://arxiv.org/abs/2011.06132v1 | BLEU score | 32.87 |
Machine Translation | WMT2016 English-Romanian | FlowSeq-large (NPD n = 30) | https://arxiv.org/abs/1909.02480v3 | BLEU score | 32.35 |
Machine Translation | WMT2016 English-Romanian | FlowSeq-large (NPD n=15) | https://arxiv.org/abs/1909.02480v3 | BLEU score | 31.97 |
Machine Translation | WMT2016 English-Romanian | FlowSeq-large (IWD n = 15) | https://arxiv.org/abs/1909.02480v3 | BLEU score | 31.08 |
Machine Translation | WMT2016 English-Romanian | CMLM+LAT+1 iterations | https://arxiv.org/abs/2011.06132v1 | BLEU score | 30.74 |
Machine Translation | WMT2016 English-Romanian | ConvS2S BPE40k | http://arxiv.org/abs/1705.03122v3 | BLEU score | 29.9 |
Machine Translation | WMT2016 English-Romanian | FlowSeq-large | https://arxiv.org/abs/1909.02480v3 | BLEU score | 29.86 |
Machine Translation | WMT2016 English-Romanian | NAT +FT + NPD | http://arxiv.org/abs/1711.02281v2 | BLEU score | 29.79 |
Machine Translation | WMT2016 English-Romanian | Denoising autoencoders (non-autoregressive) | http://arxiv.org/abs/1802.06901v3 | BLEU score | 29.66 |
Machine Translation | WMT2016 English-Romanian | FlowSeq-base | https://arxiv.org/abs/1909.02480v3 | BLEU score | 29.26 |
Machine Translation | WMT2016 English-Romanian | GRU BPE90k | https://aclanthology.org/W16-2320 | BLEU score | 28.9 |
Machine Translation | WMT2016 English-Romanian | BiGRU | http://arxiv.org/abs/1606.02891v2 | BLEU score | 28.1 |
Machine Translation | WMT2016 English-Romanian | Deep Convolutional Encoder; single-layer decoder | http://arxiv.org/abs/1611.02344v3 | BLEU score | 27.8 |
Machine Translation | WMT2016 English-Romanian | BiLSTM | http://arxiv.org/abs/1611.02344v3 | BLEU score | 27.5 |
Machine Translation | WMT2016 English-Romanian | PBSMT + NMT | http://arxiv.org/abs/1804.07755v2 | BLEU score | 25.13 |
Machine Translation | WMT2016 English-Romanian | Unsupervised PBSMT | http://arxiv.org/abs/1804.07755v2 | BLEU score | 21.33 |
Machine Translation | WMT2016 English-Romanian | Unsupervised NMT + Transformer | http://arxiv.org/abs/1804.07755v2 | BLEU score | 21.18 |
Machine Translation | WMT2016 English-Romanian | FLAN 137B (few-shot, k=9) | https://arxiv.org/abs/2109.01652v5 | BLEU score | 20.5 |
Machine Translation | WMT2016 English-Romanian | FLAN 137B (zero-shot) | https://arxiv.org/abs/2109.01652v5 | BLEU score | 18.9 |
Machine Translation | WMT2016 English-Romanian | BART (TextBox 2.0) | https://arxiv.org/abs/2212.13005v1 | BLEU-4 | 37.2 |
Machine Translation | WMT2014 English-French | Transformer+BT (ADMIN init) | https://arxiv.org/abs/2008.07772v2 | BLEU score | 46.4 |
Machine Translation | WMT2014 English-French | Transformer+BT (ADMIN init) | https://arxiv.org/abs/2008.07772v2 | SacreBLEU | 44.4 |
Machine Translation | WMT2014 English-French | Noisy back-translation | http://arxiv.org/abs/1808.09381v2 | BLEU score | 45.6 |
Machine Translation | WMT2014 English-French | Noisy back-translation | http://arxiv.org/abs/1808.09381v2 | SacreBLEU | 43.8 |
Machine Translation | WMT2014 English-French | Noisy back-translation | http://arxiv.org/abs/1808.09381v2 | Hardware Burden | 180G |
Machine Translation | WMT2014 English-French | Noisy back-translation | http://arxiv.org/abs/1808.09381v2 | Operations per network pass | null |
Machine Translation | WMT2014 English-French | mRASP+Fine-Tune | https://arxiv.org/abs/2010.03142v3 | BLEU score | 44.3 |
Machine Translation | WMT2014 English-French | mRASP+Fine-Tune | https://arxiv.org/abs/2010.03142v3 | SacreBLEU | 41.7 |
Machine Translation | WMT2014 English-French | mRASP+Fine-Tune | https://arxiv.org/abs/2010.03142v3 | Hardware Burden | null |
Machine Translation | WMT2014 English-French | mRASP+Fine-Tune | https://arxiv.org/abs/2010.03142v3 | Operations per network pass | null |
Machine Translation | WMT2014 English-French | Transformer + R-Drop | https://arxiv.org/abs/2106.14448v2 | BLEU score | 43.95 |
Machine Translation | WMT2014 English-French | Transformer + R-Drop | https://arxiv.org/abs/2106.14448v2 | Hardware Burden | null |
Machine Translation | WMT2014 English-French | Transformer + R-Drop | https://arxiv.org/abs/2106.14448v2 | Operations per network pass | null |
Machine Translation | WMT2014 English-French | Transformer (ADMIN init) | https://arxiv.org/abs/2008.07772v2 | BLEU score | 43.8 |
Machine Translation | WMT2014 English-French | Transformer (ADMIN init) | https://arxiv.org/abs/2008.07772v2 | SacreBLEU | 41.8 |
Machine Translation | WMT2014 English-French | Admin | https://arxiv.org/abs/2004.08249v3 | BLEU score | 43.8 |
Machine Translation | WMT2014 English-French | Admin | https://arxiv.org/abs/2004.08249v3 | Hardware Burden | null |
Machine Translation | WMT2014 English-French | Admin | https://arxiv.org/abs/2004.08249v3 | Operations per network pass | null |
Machine Translation | WMT2014 English-French | BERT-fused NMT | https://arxiv.org/abs/2002.06823v1 | BLEU score | 43.78 |
Machine Translation | WMT2014 English-French | MUSE(Paralllel Multi-scale Attention) | https://arxiv.org/abs/1911.09483v1 | BLEU score | 43.5 |
Machine Translation | WMT2014 English-French | MUSE(Paralllel Multi-scale Attention) | https://arxiv.org/abs/1911.09483v1 | Hardware Burden | null |
Machine Translation | WMT2014 English-French | MUSE(Paralllel Multi-scale Attention) | https://arxiv.org/abs/1911.09483v1 | Operations per network pass | null |
Machine Translation | WMT2014 English-French | T5 | https://arxiv.org/abs/1910.10683v4 | BLEU score | 43.4 |
Machine Translation | WMT2014 English-French | Local Joint Self-attention | https://arxiv.org/abs/1905.06596v1 | BLEU score | 43.3 |
Machine Translation | WMT2014 English-French | Local Joint Self-attention | https://arxiv.org/abs/1905.06596v1 | Hardware Burden | null |
Machine Translation | WMT2014 English-French | Local Joint Self-attention | https://arxiv.org/abs/1905.06596v1 | Operations per network pass | null |
Machine Translation | WMT2014 English-French | Depth Growing | https://arxiv.org/abs/1907.01968v1 | BLEU score | 43.27 |
Machine Translation | WMT2014 English-French | Depth Growing | https://arxiv.org/abs/1907.01968v1 | Hardware Burden | 24G |
Machine Translation | WMT2014 English-French | Depth Growing | https://arxiv.org/abs/1907.01968v1 | Operations per network pass | null |
Machine Translation | WMT2014 English-French | Transformer Big | http://arxiv.org/abs/1806.00187v3 | BLEU score | 43.2 |
Machine Translation | WMT2014 English-French | Transformer Big | http://arxiv.org/abs/1806.00187v3 | Hardware Burden | 55G |
Machine Translation | WMT2014 English-French | Transformer Big | http://arxiv.org/abs/1806.00187v3 | Operations per network pass | null |
Machine Translation | WMT2014 English-French | DynamicConv | http://arxiv.org/abs/1901.10430v2 | BLEU score | 43.2 |
Machine Translation | WMT2014 English-French | TaLK Convolutions | https://arxiv.org/abs/2002.03184v2 | BLEU score | 43.2 |
Machine Translation | WMT2014 English-French | LightConv | http://arxiv.org/abs/1901.10430v2 | BLEU score | 43.1 |
Machine Translation | WMT2014 English-French | FLOATER-large | https://arxiv.org/abs/2003.09229v1 | BLEU score | 42.7 |
Machine Translation | WMT2014 English-French | FLOATER-large | https://arxiv.org/abs/2003.09229v1 | Hardware Burden | null |
Machine Translation | WMT2014 English-French | FLOATER-large | https://arxiv.org/abs/2003.09229v1 | Operations per network pass | null |
Machine Translation | WMT2014 English-French | OmniNetP | https://arxiv.org/abs/2103.01075v1 | BLEU score | 42.6 |
Machine Translation | WMT2014 English-French | OmniNetP | https://arxiv.org/abs/2103.01075v1 | Hardware Burden | null |
Machine Translation | WMT2014 English-French | OmniNetP | https://arxiv.org/abs/2103.01075v1 | Operations per network pass | null |
Machine Translation | WMT2014 English-French | Transformer Big + MoS | https://arxiv.org/abs/1809.09296v2 | BLEU score | 42.1 |
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