A Parallel-Hierarchical Model for Machine Comprehension on Sparse Data

ACL 2016 Adam Trischler • Zheng Ye • Xingdi Yuan • Jing He • Phillip Bachman • Kaheer Suleman

In this work, we investigate machine comprehension on the challenging {\it MCTest} benchmark. We tackle the dataset with a neural approach, harnessing simple neural networks arranged in a parallel hierarchy. The parallel hierarchy enables our model to compare the passage, question, and answer from a variety of trainable perspectives, as opposed to using a manually designed, rigid feature set.

Full paper

Evaluation


Task Dataset Model Metric name Metric value Global rank Compare
Question Answering MCTest-160 syntax, frame, coreference, and word embedding features Accuracy 75.27% # 1
Question Answering MCTest-500 syntax, frame, coreference, and word embedding features Accuracy 69.94% # 2
Question Answering MCTest-500 Parallel-Hierarchical Accuracy 71% # 1