Year: 2021
Type: conference-paper
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 Transformerbased 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.
Authors: Zhengyuan Liu, Nancy Chen
Institutions: Institute for Infocomm Research, University of Pennsylvania
Language: English
Topic: Speech and dialogue systems
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Funder: Institute for Infocomm Research
OpenAlex ID: W3206379359