  We introduce a novel approach to transformers that learns hierarchical
representations in multiparty dialogue. First, three language modeling tasks
are used to pre-train the transformers, token- and utterance-level language
modeling and utterance order prediction, that learn both token and utterance
embeddings for better understanding in dialogue contexts. Then, multi-task
learning between the utterance prediction and the token span prediction is
applied to fine-tune for span-based question answering (QA). Our approach is
evaluated on the FriendsQA dataset and shows improvements of 3.8% and 1.4% over
the two state-of-the-art transformer models, BERT and RoBERTa, respectively.
