Dual Co-Matching Network for Multi-choice Reading Comprehension
This addresses the problem of complex reasoning in reading comprehension for AI systems, representing an incremental improvement over existing methods.
The paper tackles multi-choice reading comprehension by proposing the Dual Co-Matching Network (DCMN), which models bidirectional relationships among passage, question, and answer, achieving new state-of-the-art results on the RACE dataset.
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.