2,447 research outputs found
Dual Co-Matching Network for Multi-choice Reading Comprehension
Multi-choice reading comprehension is a challenging task that requires
complex reasoning procedure. Given passage and question, a correct answer need
to be selected from a set of candidate answers. In this paper, we propose
\textbf{D}ual \textbf{C}o-\textbf{M}atching \textbf{N}etwork (\textbf{DCMN})
which model the relationship among passage, question and answer
bidirectionally. Different from existing approaches which only calculate
question-aware or option-aware passage representation, we calculate
passage-aware question representation and passage-aware answer representation
at the same time. To demonstrate the effectiveness of our model, we evaluate
our model on a large-scale multiple choice machine reading comprehension
dataset (i.e. RACE). Experimental result show that our proposed model achieves
new state-of-the-art results.Comment: arXiv admin note: text overlap with arXiv:1806.04068 by other author
- …