@inproceedings{liu-chen-2021-improving,
title = "Improving Multi-Party Dialogue Discourse Parsing via Domain Integration",
author = "Liu, Zhengyuan and
Chen, Nancy",
editor = "Braud, Chlo{\'e} and
Hardmeier, Christian and
Li, Junyi Jessy and
Louis, Annie and
Strube, Michael and
Zeldes, Amir",
booktitle = "Proceedings of the 2nd Workshop on Computational Approaches to Discourse",
month = nov,
year = "2021",
address = "Punta Cana, Dominican Republic and Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.codi-main.11",
doi = "10.18653/v1/2021.codi-main.11",
pages = "122--127",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T Improving Multi-Party Dialogue Discourse Parsing via Domain Integration
%A Liu, Zhengyuan
%A Chen, Nancy
%Y Braud, Chloé
%Y Hardmeier, Christian
%Y Li, Junyi Jessy
%Y Louis, Annie
%Y Strube, Michael
%Y Zeldes, Amir
%S Proceedings of the 2nd Workshop on Computational Approaches to Discourse
%D 2021
%8 November
%I Association for Computational Linguistics
%C Punta Cana, Dominican Republic and Online
%F liu-chen-2021-improving
%X 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.
%R 10.18653/v1/2021.codi-main.11
%U https://aclanthology.org/2021.codi-main.11
%U https://doi.org/10.18653/v1/2021.codi-main.11
%P 122-127
Markdown (Informal)
[Improving Multi-Party Dialogue Discourse Parsing via Domain Integration](https://aclanthology.org/2021.codi-main.11) (Liu & Chen, CODI 2021)
ACL