Who Is Speaking to Whom? Learning to Identify Utterance Addressee in Multi-Party Conversations

Abstract

Previous research on dialogue systems generally focuses on the conversation between two participants, yet multi-party conversations which involve more than two participants within one session bring up a more complicated but realistic scenario. In real multiparty conversations, we can observe who is speaking, but the addressee information is not always explicit. In this paper, we aim to tackle the challenge of identifying all the missing addressees in a conversation session. To this end, we introduce a novel who-to-whom (W2W) model which models users and utterances in the session jointly in an interactive way. We conduct experiments on the benchmark Ubuntu Multi-Party Conversation Corpus and the experimental results demonstrate that our model outperforms baselines with consistent improvements.

MPC-BERT: A Pre-Trained Language Model for Multi-Party Conversation Understanding

“…Meng et al (2018) proposed a task of speaker classification as a surrogate task for speaker modeling. Le et al (2019) proposed a who-to-whom (W2W) model to recognize the addressees of all utterances. proposed a graph-structured network (GSN) to model the graphical information flow for response generation.…”

“…Given a multi-party conversation where part of the addressees are unknown, Ouchi and Tsuboi (2016) and Zhang et al (2018a) recognized an addressee of the last utterance. Le et al (2019) recognized addressees of all utterances in a conversation. In this paper, we follow the more challenging setting in Le et al (2019).…”

“…2015; Serban et al, 2016Serban et al, , 2017Zhang et al, 2018bZhang et al, , 2020 or retrieval-based manner (Lowe et al, 2015;Wu et al, 2017;Zhou et al, 2018;Tao et al, 2019a,b;Gu et al, 2019aGu et al, ,b, 2020. Recently, researchers have paid more attention to a more practical and challenging scenario involving more than two participants, which is well known as multiparty conversation (MPC) (Ouchi and Tsuboi, 2016;Zhang et al, 2018a;Le et al, 2019;. Table 1 shows an MPC example in the Ubuntu Internet Relay Chat (IRC) channel, which is composed of a sequence of (speaker, utterance, addressee) triples.…”

“…Table 1 shows an MPC example in the Ubuntu Internet Relay Chat (IRC) channel, which is composed of a sequence of (speaker, utterance, addressee) triples. In addition to returning an appropriate response, predicting who will be the next speaker (Meng et al, 2018) and who is the addressee of an utterance (Ouchi and Tsuboi, 2016;Zhang et al, 2018a;Le et al, 2019) are unique and important issues in MPC. An instance of MPC always contains complicated interactions between interlocutors, between utterances and between an interlocutor and an utterance.…”

Response Selection for Multi-Party Conversations with Dynamic Topic Tracking

“…The side effect of multi-speaker multi-turn context is crucial; a lot of noise will be introduced in the context utterances. The speaker and addressee information are essential to decide the structure of conversation, thus can also benefit conversational response selection (Zhang et al, 2017;Le et al, 2019;. A hard context retrieval method was proposed by Wu et al (2020b) to minimize the context size, while keeping only the utterances whose speaker is the same as the response candidates or referred by the response candidates.…”