  In this work, we focus on dialogue reading comprehension (DRC), a task
extracting answer spans for questions from dialogues. Dialogue context modeling
in DRC is tricky due to complex speaker information and noisy dialogue context.
To solve the two problems, previous research proposes two self-supervised tasks
respectively: guessing who a randomly masked speaker is according to the
dialogue and predicting which utterance in the dialogue contains the answer.
Although these tasks are effective, there are still urging problems: (1)
randomly masking speakers regardless of the question cannot map the speaker
mentioned in the question to the corresponding speaker in the dialogue, and
ignores the speaker-centric nature of utterances. This leads to wrong answer
extraction from utterances in unrelated interlocutors' scopes; (2) the single
utterance prediction, preferring utterances similar to the question, is limited
in finding answer-contained utterances not similar to the question. To
alleviate these problems, we first propose a new key utterances extracting
method. It performs prediction on the unit formed by several contiguous
utterances, which can realize more answer-contained utterances. Based on
utterances in the extracted units, we then propose Question-Interlocutor Scope
Realized Graph (QuISG) modeling. As a graph constructed on the text of
utterances, QuISG additionally involves the question and question-mentioning
speaker names as nodes. To realize interlocutor scopes, speakers in the
dialogue are connected with the words in their corresponding utterances.
Experiments on the benchmarks show that our method can achieve better and
competitive results against previous works.

Computation and Language