Speaker-Aware Discourse Parsing on Multi-Party Dialogues (2022)

Abstract: Discourse parsing on multi-party dialogues is an important but difficult task in dialogue systems and conversational analysis. It is believed that speaker interactions are helpful for this task. However, most previous research ignores speaker interactions between different speakers. To this end, we present a speaker-aware model for this task. Concretely, we propose a speaker-context interaction joint encoding (SCIJE) approach, using the interaction features between different speakers. In addition, we propose a second-stage pre-training task, same speaker prediction (SSP), enhancing the conversational context representations by predicting whether two utterances are from the same speaker. Experiments on two standard benchmark datasets show that the proposed model achieves the best-reported performance in the literature. We will release the codes of this paper to facilitate future research.

## introduction discourse parsing on multi-party dialogues aims to identify the discourse relations between utterances in dialogues, which has received increasing attention in the natural language processing (nlp) community @xcite @xcite . unlike traditional text-level discourse parsing based on the rhetorical structure theory (rst) @xcite and the penn discourse tree-bank (pdtb) @xcite , this task is performed based on the segmented discourse relation theory (sdrt) @xcite . it represents a multi-party dialogue by a discourse dependency tree @xcite . figure 1 shows an example. the leaf nodes are utterances, and the arcs indicate the discourse relations between utterances. each utterance is referred as an elementary discourse unit (edu) in sdrt discourse parsing. u 1 , u 2 , u 3 , u 4 , u 5 refer to edus. "q-elab", "qap", "q-elab", and "elab" refer to discourse relations. "a", "b", and "c" are three speakers. a multi-party dialogue has several aspects that make its discourse parsing more challenging than that of a written text created by one author. it involves multiple speakers who interact with each other in different roles during turn shifting and make contributions to the interactions with multiple potential threads @xcite . therefore, in addition to conversational contexts, speaker interactions are also important cues in determining the discourse structure of a multi-party dialogue. most current research for discourse parsing on multi-party dialogues focuses on conversational context modeling with different methods. a pioneer study by @xcite adopts a statistical model for this task, using human-designed features extracted from conversational contexts, while an early neural research by @xcite proposes a deep sequential model, using hierarchical grus to learn conversational contextual cues for discourse parsing. recent research exerts more efforts on integrating rich information with context modeling and explores different techniques such as domain adaptation @xcite , edgecentric encoding @xcite , multi-task learning @xcite , and joint model @xcite . although above approaches give competitive performances on discourse parsing on multi-party dialogues, only a few studies @xcite @xcite consider speaker interactions. these studies use edu pair features to represent speaker interactions and demonstrate that introducing speaker interactions is beneficial to this task. however, the edu pair based speaker interaction modeling only represent whether an edu pair is from the same speaker. the informative interaction between different speakers remains unexplored. as shown in figure 1 , the connected edus u 1 and u 5 are from two different speakers, but the edu pair features will not clearly tell who said the edus. since a general multiparty dialogue involves more than two speakers, the problem could be extremely serious. to alleviate the above problem, we propose a speaker-aware model for discourse parsing on multi-party dialogues. concretely, to handle the interactive information between the same speaker within a dialogue, we present ssp-bert, a secondstage pre-training method based on bert that is designed to predict whether two edus are from the same speaker. based on ssp-bert, we investigate a speaker-context interactions joint encoding (scije) approach to handle the interactions between different speakers. first, we follow the node-centric based encoding approach (shi and huang, 2019; liu and chen, 2021), adopting bert and bigru to represent conversational contexts. then we embed the speaker sequence of each dialogue to vectors and feed them into bigru to further obtain speaker interaction representations. we finally combine them and thus obtain speakercontext interaction joint representations. we conduct experiments on stac @xcite and molweni @xcite to evaluate our proposed model. experimental results show that ssp-bert is highly competitive for discourse parsing on multi-party dialogues. when the speakercontext interaction joint representations are integrated, the proposed model is able to obtain further improvements. our proposed model achieves the best performance among all the state-of-theart (sota) models reported in the literature. in summary, we mainly make the following three contributions in this paper: ## related work text-level discourse parsing can be categorized into two types: the rst-style @xcite and the pdtb-style @xcite parsing. both tasks have been intensively investigated since early @xcite . compared with text-level discourse parsing, discourse parsing on multi-party dialogues is still at its early stage. the pioneer study @xcite mainly borrows the dependency parsing paradigm from rst-style parsing @xcite for this task, using human-designed features. recently, inspired by the success of neural discourse parsing models @xcite @xcite , several neural discourse parsing models for multiparty dialogues have been proposed as well @xcite @xcite @xcite . in this paper, we follow the line of the work using neural models to this task. it is believed that speaker interactions are helpful for modeling multi-party dialogues, giving great improvements on language modeling @xcite , dialogue comprehension @xcite . in discourse parsing, @xcite extract hand-crafted features from the edu pair that have the same speaker, and feed them into a statistical discourse parsing model. shi and huang (2019) use a speaker highlight mechanism to represent speaker interactions. @xcite treat speaker interactions as edges of edus, feeding them into graph neural network (gnn) to obtain edge-centric representations. however, these speaker interaction models based on edu pairs only indicate whether two edus are from the same speaker, ignoring the interactions between different speakers. in this paper, we investigate the interaction features between different speakers, using them as a strong supplementary for the context representations. recent research investigates pre-training on dialogues intensively @xcite @xcite @xcite . almost all studies focus on capturing coherance between utterances by using pre-training tasks such as dialogue generation or response selection. in this work, we enhance the conversational context representations with interaction features between the same speaker. ## development experiments in this section, we conduct development experiments to examine the effectiveness of some important factors on our proposed model. ## main results and analysis main results here we report the final results of the proposed model over the molweni and the stac test sets. as shown in ## conclusion in this paper, we proposed a speaker-aware model for discourse parsing on multi-party dialogues, it is able to better model the speaker interactions for this task. first, we proposed scije to incorporate the interaction features between the different speakers. second, we integrated the interaction features between the same speaker to the conversational context representations by exploiting ssp-bert. we conducted experiments and analysis on two standard benchmark datasets, namely stac @xcite and molweni @xcite . results show that our proposed speaker-aware discourse parsing model significantly outperforms previous sota systems in the literature.