Building Large Machine Reading-Comprehension Datasets using Paragraph Vectors

Radu Soricut; Nan Ding

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arXiv · 1612.04342

Building Large Machine Reading-Comprehension Datasets using Paragraph Vectors

Radu Soricut·Nan Ding

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Abstract

We present a dual contribution to the task of machine reading-comprehension: a technique for creating large-sized machine-comprehension (MC) datasets using paragraph-vector models; and a novel, hybrid neural-network architecture that combines the representation power of recurrent neural networks with the discriminative power of fully-connected multi-layered networks. We use the MC-dataset generation technique to build a dataset of around 2 million examples, for which we empirically determine the high-ceiling of human performance (around 91% accuracy), as well as the performance of a variety of computer models. Among all the models we have experimented with, our hybrid neural-network architecture achieves the highest performance (83.2% accuracy). The remaining gap to the human-performance ceiling provides enough room for future model improvements.

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BibTeX RIS

Radu Soricut, Nan Ding. 2016-12-13. Building Large Machine Reading-Comprehension Datasets using Paragraph Vectors. https://arxiv.org/abs/1612.04342

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