Improving Multi-Party Dialogue Discourse Parsing via Domain Integration (2021)

Abstract: While multi-party conversations are often less structured than monologues and documents, they are implicitly organized by semantic level correlations across the interactive turns, and dialogue discourse analysis can be applied to predict the dependency structure and relations between the elementary discourse units, and provide feature-rich structural information for downstream tasks. However, the existing corpora with dialogue discourse annotation are collected from specific domains with limited sample sizes, rendering the performance of data-driven approaches poor on incoming dialogues without any domain adaptation. In this paper, we first introduce a Transformer-based parser, and assess its cross-domain performance. We next adopt three methods to gain domain integration from both data and language modeling perspectives to improve the generalization capability. Empirical results show that the neural parser can benefit from our proposed methods, and performs better on cross-domain dialogue samples.

## corpora analysis in this section, we conduct a statistical analysis of three text-level discourse treebanks for data-related factors that potentially affect model generality. rst discourse treebank (rst-dt) @xcite is the first corpus for text-level document parsing, and contains articles from the wall street journal (wsj). while it is not in the dialogue domain, we include it for an extensive comparison. stac @xcite is the first corpus for multi-party dialogue discourse parsing, and built on 1.2k strategic conversations where participants take discussion during playing an online game. molweni @xcite follows the same annotation scheme as stac, and the data (12k samples) are collected from an online forum, where people discuss technical topics about the ubuntu system. the data statistics are summarized in ## cross-domain integration based on the corpora analysis in section 2, to improve the domain-level generality, we investigate three methods to encourage the neural model to utilize the shared linguistic features from different dialogue domains. ## conclusion in this paper, we investigated the domain-level generality of dialogue discourse parsing. since existing corpora are collected from different conversation scenarios, models with single-domain training cannot perform well in other domains. the statistical analysis and experimental results suggest that domain adaptation or integration is necessary when neural parsers are applied in practical use cases, and utilizing prior language knowledge and adopting cross-domain pre-training can improve their generality.