ERNIE 2.0 (Enhanced Representation through kNowledge IntEgration), a new knowledge integration language representation model that aims to beat SOTA results of BERT and XLNet. While pre-training with more than just several simple tasks to grasp the co-occurrence of words or sentences for language modeling, Ernie aims to explore named entities, semantic closeness and discourse relations to obtain valuable lexical, syntactic and semantic information from training corpora. Ernie 2.0 focus on building and learning incrementally pre-training tasks through constant multi-task learning. And it brings some interesting results.

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Transformer - more than meets the eye! Are we there yet? Well... not really, but...
How about eliminating recurrence and convolution from transduction? Sequence modeling and transduction (e.g. language modeling, machine translation) problems solutions has been dominated by RNN (especially gated RNN) or LSTM, additionally employing the attention mechanism. Main sequence transduction models are based on RNN or CNN including encoder and decoder. The new transformer architecture is claimed however, to be more parallelizable and requiring significantly less time to train, solely focusing on attention mechanisms.

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