Deep Learning

This talk summarizes the paper [`Language Models are Few-Shot Learners`](https://arxiv.org/abs/2005.14165).

We propose AugVic, a data augmentation framework for sequence to sequence model (i.e. NMT) using Language Model.

We propose UXLA, a novel data augmentation framework for self-supervised learning in zero-resource transfer learning scenarios.

We propose a novel semi-supervised method to learn cross-lingual word embeddings for BLI.

This talk summarizes the paper [`mBART`](https://arxiv.org/abs/2001.08210) and some pretraining concepts.

We propose a superior model and training method for zero resource transfer of Cross-lingual Named Entity Recognition.

This talk summarizes the paper [`mixup`](https://arxiv.org/abs/1710.09412), [`MixMatch`](https://arxiv.org/abs/1905.02249), [`DivideMix`](https://openreview.net/pdf?id=HJgExaVtwr), [`UDA`](https://arxiv.org/abs/1904.12848).

This talk summarizes the paper [`Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context`](https://arxiv.org/abs/1901.02860). It assumes that audience are already familier with [`Attention Is All You Need`](https://arxiv.org/abs/1706.03762) paper and also discuss some high level concepts of it.

This is a tool to translate an English sentence into Malay and vice versa.

This talk summarizes the paper [`Fast and Accurate Entity Recognition with Iterated Dilated Convolutions`](https://arxiv.org/abs/1702.02098).