Role Aware Multi-Party Dialogue Question Answering

Multi-party dialogue question answering (MPDQA) is an emerging topic in speech and language processing where the goal is to answer the questions according to the multiparty conversations. Different from conventional QA, which assumes a single speaker (writer) and general listeners (readers), MPDQA involves multiple speakers and specific listeners. Prior works simply treat the dialogues as plain passages, which neglect the importance of role awareness, such as speakers’ perspectives and co-references. In a novel aspect, this paper proposes the Role Aware Multi-Party Network (RAMPNet), a model utilizing the information of speaker and role to present “who is speaking” and “who is mentioned”, making role awareness an available message for our model. Experiments show that our RAMPNet outperforms the BERT baseline model on a large-scale MPDQA dataset, FriendsQA, especially in the “Who” and “How” questions, which strongly need the ability of conversation relations understanding to answer these questions. In addition, our further analysis demonstrates RAMPNet’s effectiveness in those questions contain verbs such as “reply” or “talk”, related to interactions between speakers and roles. All of the results show the capability and utility of RAMPNet.