| Model | Dev accuracy | Test F1 | Paper / Source | Code |
|---|---|---|---|---|
| Dalal et al. (2006) | 87.40 | 82.40 | Hindi Part-of-Speech Tagging and Chunking: A Maximum Entropy Approach |
| Model | Dev accuracy | Test F1 | Paper / Source | Code |
|---|---|---|---|---|
| Jha et al. (2018) | 99.30 | 99.06 | Multi-Task Deep Morphological Analyzer: Context-Aware Joint Morphological Tagging and Lemma Prediction | mt-dma |
| Dalal et al. (2006) | 89.35 | 82.22 | Hindi Part-of-Speech Tagging and Chunking: A Maximum Entropy Approach |
The IIT Bombay English-Hindi Parallel Corpus used by Kunchukuttan et al. (2018) can be accessed here. A live leaderboard involving more directions involving Hindi can be accessed at the evaluation website for the Workshop on Asian Translation.
| Model | BLEU | Paper / Source | Code |
|---|---|---|---|
| Philip et al. (2020) | 24.85 | Revisiting Low Resource Status of Indian Languages in MT | ilmulti |
| Siripragada et al. (2020) | 22.91 | A Multilingual Parallel Corpora Collection Effort for Indian Languages | ilmulti |
| Goyal et al. (2019) | 19.06 | LTRC-MT Simple & Effective Hindi-English Neural Machine Translation Systems at WAT 2019 |
| Model | BLEU | Paper / Source | Code |
|---|---|---|---|
| Philip et al. (2018) | 21.57 | CVIT-MT Systems for WAT-2018 | |
| Philip et al. (2020) | 21.20 | Revisiting Low Resource Status of Indian Languages in MT | ilmulti |
| Saini et al. (2018) | 18.215 | Neural Machine Translation for English to Hindi |