Unlike passages, where sentences are often the default semantic modeling unit, in multi-turn conversations, a turn is a topically coherent unit embodied with immediately relevant context, making it a linguistically intuitive segment for computationally modeling verbal interactions.
in this work, we propose a hierarchical attention neural network architecture, combining turnlevel and word-level attention mechanisms, to improve spoken dialogue comprehension performance.