Search Results for author:
Found 10 papers, 5 papers with code
Natural Questions: a Benchmark for Question Answering Research
The public release consists of 307, 373 training examples with single annotations, 7, 830 examples with 5-way annotations for development data, and a further 7, 842 examples 5-way annotated sequestered as test data.
Ranked #7 on
Question Answering
on Natural Questions (long)
Improving Neural Program Synthesis with Inferred Execution Traces
The task of program synthesis, or automatically generating programs that are consistent with a provided specification, remains a challenging task in artificial intelligence.
NAPS: Natural Program Synthesis Dataset
We present a program synthesis-oriented dataset consisting of human written problem statements and solutions for these problems.
Neural Program Search: Solving Programming Tasks from Description and Examples
We present a Neural Program Search, an algorithm to generate programs from natural language description and a small number of input/output examples.
Neural Program Search: Solving Data Processing Tasks from Description and Examples
We present a Neural Program Search, an algorithm to generate programs from natural language description and a small number of input / output examples.
TensorFlow Estimators: Managing Simplicity vs. Flexibility in High-Level Machine Learning Frameworks
Our focus is on simplifying cutting edge machine learning for practitioners in order to bring such technologies into production.
Coarse-to-Fine Question Answering for Long Documents
We present a framework for question answering that can efficiently scale to longer documents while maintaining or even improving performance of state-of-the-art models.
Attention Is All You Need
The dominant sequence transduction models are based on complex recurrent or convolutional neural networks in an encoder-decoder configuration.
Ranked #2 on
Multimodal Machine Translation
on Multi30K
(BLUE (DE-EN) metric)
Hierarchical Question Answering for Long Documents
We present a framework for question answering that can efficiently scale to longer documents while maintaining or even improving performance of state-of-the-art models.
WikiReading: A Novel Large-scale Language Understanding Task over Wikipedia
The task contains a rich variety of challenging classification and extraction sub-tasks, making it well-suited for end-to-end models such as deep neural networks (DNNs).
