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natural_language_processing/README.md

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| [Sequence to Sequence Learning with Neural Networks](https://arxiv.org/abs/1409.3215) | [HackMD](https://hackmd.io/@photon-dodo/HyrN0wjkv) | [Rishika](https://https://github.com/rishika2110) [Khurshed](https://https://github.com/GlazeDonuts) | This paper presents a novel architecture and paradigm for Neural Machine Translation. |
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| [Neural Machine Translation by jointly learning to align and translate]( https://arxiv.org/abs/1409.0473) | [HackMD]( https://hackmd.io/@photon-dodo/HJfefAQbP) | [Rishika](https://https://github.com/rishika2110) [Khurshed](https://https://github.com/GlazeDonuts) | This paper introduces a novel attention based approach for translation. |
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| [Answer Them All! Toward Universal Visual Question Answering Models](https://arxiv.org/abs/1903.00366) | [Notion](https://phrygian-macaroni-e3b.notion.site/Answer-Them-All-RAMEN-d441cd8797984474baeba5ce4176956d) | [Aneesh](https://sites.google.com/view/aneesh-shetye/home) | This paper tries to resolve the disparity in performance of previous Visual Question Answering (VQA) architectures on synthetic and natural datasets by introducing a novel architecture. |

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