In this work, we introduce a hierarchical neu- ral attention architecture, integrating turn-level attention with word-level attention for multi-turn dialogue comprehension in a question-answering manner, where we evaluate performance on a corpus preserving linguistic features from real-world spoken conversation scenarios.
we examine how our approach is able to address challenges from limited training data scenarios and from lengthy and out-of-distribution test samples.