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BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
TLDR
A new language representation model, BERT, designed to pre-train deep bidirectional representations from unlabeled text by jointly conditioning on both left and right context in all layers, which can be fine-tuned with just one additional output layer to create state-of-the-art models for a wide range of tasks.
Natural Questions: A Benchmark for Question Answering Research
TLDR
The Natural Questions corpus, a question answering data set, is presented, introducing robust metrics for the purposes of evaluating question answering systems; demonstrating high human upper bounds on these metrics; and establishing baseline results using competitive methods drawn from related literature.
Fast and Robust Neural Network Joint Models for Statistical Machine Translation
TLDR
A novel formulation for a neural network joint model (NNJM), which augments the NNLM with a source context window, which is purely lexicalized and can be integrated into any MT decoder.
RobustFill: Neural Program Learning under Noisy I/O
TLDR
This work directly compares both approaches for automatic program learning on a large-scale, real-world learning task and demonstrates that the strength of each approach is highly dependent on the evaluation metric and end-user application.
Visual Storytelling
TLDR
Modelling concrete description as well as figurative and social language, as provided in this dataset and the storytelling task, has the potential to move artificial intelligence from basic understandings of typical visual scenes towards more and more human-like understanding of grounded event structure and subjective expression.
Universal Neural Machine Translation for Extremely Low Resource Languages
TLDR
The proposed approach utilizing a transfer-learning approach to share lexical and sentence level representations across multiple source languages into one target language is able to achieve 23 BLEU on Romanian-English WMT2016 using a tiny parallel corpus of 6k sentences.
Leveraging Grammar and Reinforcement Learning for Neural Program Synthesis
TLDR
Reinforcement learning is performed on top of a supervised model with an objective that explicitly maximizes the likelihood of generating semantically correct programs, which leads to improved accuracy of the models, especially in cases where the training data is limited.
Language Models for Image Captioning: The Quirks and What Works
TLDR
By combining key aspects of the ME and RNN methods, this paper achieves a new record performance over previously published results on the benchmark COCO dataset, however, the gains the authors see in BLEU do not translate to human judgments.
Synthetic QA Corpora Generation with Roundtrip Consistency
TLDR
A novel method of generating synthetic question answering corpora is introduced by combining models of question generation and answer extraction, and by filtering the results to ensure roundtrip consistency, establishing a new state-of-the-art on SQuAD2 and NQ.
Generating Natural Questions About an Image
TLDR
This paper introduces the novel task of Visual Question Generation, where the system is tasked with asking a natural and engaging question when shown an image, and provides three datasets which cover a variety of images from object-centric to event-centric.
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