Question-Interlocutor Scope Realized Graph Modeling over Key Utterances for Dialogue Reading Comprehension

by   Jiangnan Li, et al.

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.


Self- and Pseudo-self-supervised Prediction of Speaker and Key-utterance for Multi-party Dialogue Reading Comprehension

Multi-party dialogue machine reading comprehension (MRC) brings tremendo...

Multi-turn Dialogue Reading Comprehension with Pivot Turns and Knowledge

Multi-turn dialogue reading comprehension aims to teach machines to read...

Channel-aware Decoupling Network for Multi-turn Dialogue Comprehension

Training machines to understand natural language and interact with human...

Coreference-aware Double-channel Attention Network for Multi-party Dialogue Reading Comprehension

We tackle Multi-party Dialogue Reading Comprehension (abbr., MDRC). MDRC...

Speaking the Language of Your Listener: Audience-Aware Adaptation via Plug-and-Play Theory of Mind

Dialogue participants may have varying levels of knowledge about the top...

Construction Repetition Reduces Information Rate in Dialogue

Speakers repeat constructions frequently in dialogue. Due to their pecul...

Should Answer Immediately or Wait for Further Information? A Novel Wait-or-Answer Task and Its Predictive Approach

Different people have different habits of describing their intents in co...

Please sign up or login with your details

Forgot password? Click here to reset