@inproceedings{lopez-cortez-jacobs-2023-incorporating,
title = "Incorporating Annotator Uncertainty into Representations of Discourse Relations",
author = "L{\'o}pez Cortez, S. Magal{\'\i} and
Jacobs, Cassandra L.",
editor = "Stoyanchev, Svetlana and
Joty, Shafiq and
Schlangen, David and
Dusek, Ondrej and
Kennington, Casey and
Alikhani, Malihe",
booktitle = "Proceedings of the 24th Annual Meeting of the Special Interest Group on Discourse and Dialogue",
month = sep,
year = "2023",
address = "Prague, Czechia",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.sigdial-1.49",
doi = "10.18653/v1/2023.sigdial-1.49",
pages = "530--537",
abstract = "Annotation of discourse relations is a known difficult task, especially for non-expert annotators. In this paper, we investigate novice annotators{'} uncertainty on the annotation of discourse relations on spoken conversational data. We find that dialogue context (single turn, pair of turns within speaker, and pair of turns across speakers) is a significant predictor of confidence scores. We compute distributed representations of discourse relations from co-occurrence statistics that incorporate information about confidence scores and dialogue context. We perform a hierarchical clustering analysis using these representations and show that weighting discourse relation representations with information about confidence and dialogue context coherently models our annotators{'} uncertainty about discourse relation labels.",
}
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<abstract>Annotation of discourse relations is a known difficult task, especially for non-expert annotators. In this paper, we investigate novice annotators’ uncertainty on the annotation of discourse relations on spoken conversational data. We find that dialogue context (single turn, pair of turns within speaker, and pair of turns across speakers) is a significant predictor of confidence scores. We compute distributed representations of discourse relations from co-occurrence statistics that incorporate information about confidence scores and dialogue context. We perform a hierarchical clustering analysis using these representations and show that weighting discourse relation representations with information about confidence and dialogue context coherently models our annotators’ uncertainty about discourse relation labels.</abstract>
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%0 Conference Proceedings
%T Incorporating Annotator Uncertainty into Representations of Discourse Relations
%A López Cortez, S. Magalí
%A Jacobs, Cassandra L.
%Y Stoyanchev, Svetlana
%Y Joty, Shafiq
%Y Schlangen, David
%Y Dusek, Ondrej
%Y Kennington, Casey
%Y Alikhani, Malihe
%S Proceedings of the 24th Annual Meeting of the Special Interest Group on Discourse and Dialogue
%D 2023
%8 September
%I Association for Computational Linguistics
%C Prague, Czechia
%F lopez-cortez-jacobs-2023-incorporating
%X Annotation of discourse relations is a known difficult task, especially for non-expert annotators. In this paper, we investigate novice annotators’ uncertainty on the annotation of discourse relations on spoken conversational data. We find that dialogue context (single turn, pair of turns within speaker, and pair of turns across speakers) is a significant predictor of confidence scores. We compute distributed representations of discourse relations from co-occurrence statistics that incorporate information about confidence scores and dialogue context. We perform a hierarchical clustering analysis using these representations and show that weighting discourse relation representations with information about confidence and dialogue context coherently models our annotators’ uncertainty about discourse relation labels.
%R 10.18653/v1/2023.sigdial-1.49
%U https://aclanthology.org/2023.sigdial-1.49
%U https://doi.org/10.18653/v1/2023.sigdial-1.49
%P 530-537
Markdown (Informal)
[Incorporating Annotator Uncertainty into Representations of Discourse Relations](https://aclanthology.org/2023.sigdial-1.49) (López Cortez & Jacobs, SIGDIAL 2023)
ACL