# |
Team |
Task |
Date/Time |
DataID |
AMFM |
Method
|
Other Resources
|
System Description |
unuse |
unuse |
unuse |
unuse |
unuse |
unuse |
unuse |
unuse |
unuse |
unuse |
|
1 | SRPOL | INDIC21en-pa | 2021/05/04 15:22:17 | 6239 | - | - | - | - | - | - | 0.814115 | - | - | - | NMT | No | Ensemble of one-to-many on all data. Pretrained on BT, finetuned on PMI |
2 | CFILT | INDIC21en-pa | 2021/05/04 01:06:02 | 6049 | - | - | - | - | - | - | 0.813658 | - | - | - | NMT | No | Multilingual(One-to-Many(En-XX)) NMT model based on Transformer with shared encoder and decoder. |
3 | SRPOL | INDIC21en-pa | 2021/05/04 16:28:01 | 6265 | - | - | - | - | - | - | 0.813158 | - | - | - | NMT | No | One-to-many on all data. Pretrained on BT, finetuned on PMI |
4 | IIIT-H | INDIC21en-pa | 2021/05/03 18:10:46 | 6012 | - | - | - | - | - | - | 0.810972 | - | - | - | NMT | No | MNMT system (En-XX) trained via exploiting lexical similarity on PMI+CVIT parallel corpus, then improved using back translation on PMI monolingual data followed by fine tuning. |
5 | mcairt | INDIC21en-pa | 2021/05/03 17:49:01 | 6001 | - | - | - | - | - | - | 0.810106 | - | - | - | NMT | No | multilingual model(one to many model) trained on all WAT 2021 data by using base transformer. |
6 | coastal | INDIC21en-pa | 2021/05/04 01:39:49 | 6085 | - | - | - | - | - | - | 0.803382 | - | - | - | NMT | No | seq2seq model trained on all WAT2021 data |
7 | NICT-5 | INDIC21en-pa | 2021/04/22 11:53:38 | 5362 | - | - | - | - | - | - | 0.803326 | - | - | - | NMT | No | MBART+MNMT. Beam 4. |
8 | sakura | INDIC21en-pa | 2021/05/04 04:14:42 | 6158 | - | - | - | - | - | - | 0.802223 | - | - | - | NMT | No | Pre-training multilingual mBART one2many model with training corpus followed by finetuning on PMI Parallel.
|
9 | sakura | INDIC21en-pa | 2021/05/01 11:35:43 | 5889 | - | - | - | - | - | - | 0.801354 | - | - | - | NMT | No | Fine-tuning of multilingual mBART one2many model with training corpus.
|
10 | NICT-5 | INDIC21en-pa | 2021/04/21 15:45:07 | 5287 | - | - | - | - | - | - | 0.794023 | - | - | - | NMT | No | Pretrain MBART on IndicCorp and FT on bilingual PMI data. Beam search. Model is bilingual. |
11 | NLPHut | INDIC21en-pa | 2021/03/19 16:33:07 | 4598 | - | - | - | - | - | - | 0.778215 | - | - | - | NMT | No | Transformer with target language tag trained using all languages PMI data. Then fine-tuned using all en-pa data. |
12 | SRPOL | INDIC21en-pa | 2021/04/21 19:21:47 | 5322 | - | - | - | - | - | - | 0.773330 | - | - | - | NMT | No | Base transformer on all WAT21 data |
13 | ORGANIZER | INDIC21en-pa | 2021/04/08 17:25:01 | 4802 | - | - | - | - | - | - | 0.762364 | - | - | - | NMT | No | Bilingual baseline trained on PMI data. Transformer base. LR=10-3 |
14 | IITP-MT | INDIC21en-pa | 2021/05/04 18:10:01 | 6298 | - | - | - | - | - | - | 0.663206 | - | - | - | NMT | No | One-to-Many model trained on all training data with base Transformer. All indic language data is romanized. Model fine-tuned on BT PMI monolingual corpus. |
15 | gaurvar | INDIC21en-pa | 2021/05/01 19:33:20 | 5933 | - | - | - | - | - | - | 0.643473 | - | - | - | NMT | No | Multi Task Multi Lingual T5 trained for Multiple Indic Languages |
16 | gaurvar | INDIC21en-pa | 2021/04/25 20:01:24 | 5585 | - | - | - | - | - | - | 0.620318 | - | - | - | NMT | No | Multi Task Multi Lingual T5 trained for Multiple Indic Languages |
17 | NICT-5 | INDIC21en-pa | 2021/06/25 11:39:08 | 6490 | - | - | - | - | - | - | 0.000000 | - | - | - | NMT | No | Using PMI and PIB data for fine-tuning on a mbart model trained for over 5 epochs. MNMT model. |