@inproceedings{prevot-etal-2023-comparing,
title = "Comparing Methods for Segmenting Elementary Discourse Units in a {F}rench Conversational Corpus",
author = "Prevot, Laurent and
Hunter, Julie and
Muller, Philippe",
editor = {Alum{\"a}e, Tanel and
Fishel, Mark},
booktitle = "Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa)",
month = may,
year = "2023",
address = "T{\'o}rshavn, Faroe Islands",
publisher = "University of Tartu Library",
url = "https://aclanthology.org/2023.nodalida-1.44",
pages = "436--446",
abstract = "While discourse parsing has made considerable progress in recent years, discourse segmentation of conversational speech remains a difficult issue. In this paper, we exploit a French data set that has been manually segmented into discourse units to compare two approaches to discourse segmentation: fine-tuning existing systems on manual segmentation vs. using hand-crafted labelling rules to develop a weakly supervised segmenter. Our results show that both approaches yield similar performance in terms of f-score while data programming requires less manual annotation work. In a second experiment we play with the amount of training data used for fine-tuning systems and show that a small amount of hand labelled data is enough to obtain good results (although significantly lower than in the first experiment using all the annotated data available).",
}
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%0 Conference Proceedings
%T Comparing Methods for Segmenting Elementary Discourse Units in a French Conversational Corpus
%A Prevot, Laurent
%A Hunter, Julie
%A Muller, Philippe
%Y Alumäe, Tanel
%Y Fishel, Mark
%S Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa)
%D 2023
%8 May
%I University of Tartu Library
%C Tórshavn, Faroe Islands
%F prevot-etal-2023-comparing
%X While discourse parsing has made considerable progress in recent years, discourse segmentation of conversational speech remains a difficult issue. In this paper, we exploit a French data set that has been manually segmented into discourse units to compare two approaches to discourse segmentation: fine-tuning existing systems on manual segmentation vs. using hand-crafted labelling rules to develop a weakly supervised segmenter. Our results show that both approaches yield similar performance in terms of f-score while data programming requires less manual annotation work. In a second experiment we play with the amount of training data used for fine-tuning systems and show that a small amount of hand labelled data is enough to obtain good results (although significantly lower than in the first experiment using all the annotated data available).
%U https://aclanthology.org/2023.nodalida-1.44
%P 436-446
Markdown (Informal)
[Comparing Methods for Segmenting Elementary Discourse Units in a French Conversational Corpus](https://aclanthology.org/2023.nodalida-1.44) (Prevot et al., NoDaLiDa 2023)
ACL