  Comprehending a dialogue requires a model to capture diverse kinds of key
information in the utterances, which are either scattered around or implicitly
implied in different turns of conversations. Therefore, dialogue comprehension
requires diverse capabilities such as paraphrasing, summarizing, and
commonsense reasoning. Towards the objective of pre-training a zero-shot
dialogue comprehension model, we develop a novel narrative-guided pre-training
strategy that learns by narrating the key information from a dialogue input.
However, the dialogue-narrative parallel corpus for such a pre-training
strategy is currently unavailable. For this reason, we first construct a
dialogue-narrative parallel corpus by automatically aligning movie subtitles
and their synopses. We then pre-train a BART model on the data and evaluate its
performance on four dialogue-based tasks that require comprehension.
Experimental results show that our model not only achieves superior zero-shot
performance but also exhibits stronger fine-grained dialogue comprehension
capabilities. The data and code are available at
https://github.com/zhaochaocs/Diana
