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ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators

The contextual representations learned by the proposed replaced token detection pre-training task substantially outperform the ones learned by methods such as BERT and XLNet given the same model size, data, and compute.
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Unsupervised Data Augmentation for Consistency Training

A new perspective on how to effectively noise unlabeled examples is presented and it is argued that the quality of noising, specifically those produced by advanced data augmentation methods, plays a crucial role in semi-supervised learning.
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Self-Training With Noisy Student Improves ImageNet Classification

We present a simple self-training method that achieves 88.4% top-1 accuracy on ImageNet, which is 2.0% better than the state-of-the-art model that requires 3.5B weakly labeled Instagram images. On…
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QANet: Combining Local Convolution with Global Self-Attention for Reading Comprehension

A new Q\&A architecture called QANet is proposed, which does not require recurrent networks, and its encoder consists exclusively of convolution and self-attention, where convolution models local interactions andSelf-att attention models global interactions.
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Towards a Human-like Open-Domain Chatbot

Meena, a multi-turn open-domain chatbot trained end-to-end on data mined and filtered from public domain social media conversations, is presented and a human evaluation metric called Sensibleness and Specificity Average (SSA) is proposed, which captures key elements of a human-like multi- turn conversation.
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Multi-task Sequence to Sequence Learning

The results show that training on a small amount of parsing and image caption data can improve the translation quality between English and German by up to 1.5 BLEU points over strong single-task baselines on the WMT benchmarks, and reveal interesting properties of the two unsupervised learning objectives, autoencoder and skip-thought, in the MTL context.
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A Hierarchical Neural Autoencoder for Paragraphs and Documents

This paper introduces an LSTM model that hierarchically builds an embedding for a paragraph from embeddings for sentences and words, then decodes this embedding to reconstruct the original paragraph and evaluates the reconstructed paragraph using standard metrics to show that neural models are able to encode texts in a way that preserve syntactic, semantic, and discourse coherence.
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Unsupervised Data Augmentation

UDA has a small twist in that it makes use of harder and more realistic noise generated by state-of-the-art data augmentation methods, which leads to substantial improvements on six language tasks and three vision tasks even when the labeled set is extremely small.

Semi-Supervised Sequence Modeling with Cross-View Training

Cross-View Training (CVT), a semi-supervised learning algorithm that improves the representations of a Bi-LSTM sentence encoder using a mix of labeled and unlabeled data, is proposed and evaluated, achieving state-of-the-art results.
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Massive Exploration of Neural Machine Translation Architectures

This work presents a large-scale analysis of the sensitivity of NMT architectures to common hyperparameters, and reports empirical results and variance numbers for several hundred experimental runs corresponding to over 250,000 GPU hours on a WMT English to German translation task.
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