@inproceedings{cuesta-lazaro-etal-2022-sea,
title = "What does the sea say to the shore? A {BERT} based {DST} style approach for speaker to dialogue attribution in novels",
author = "Cuesta-Lazaro, Carolina and
Prasad, Animesh and
Wood, Trevor",
editor = "Muresan, Smaranda and
Nakov, Preslav and
Villavicencio, Aline",
booktitle = "Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = may,
year = "2022",
address = "Dublin, Ireland",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.acl-long.400",
doi = "10.18653/v1/2022.acl-long.400",
pages = "5820--5829",
abstract = "We present a complete pipeline to extract characters in a novel and link them to their direct-speech utterances. Our model is divided into three independent components: extracting direct-speech, compiling a list of characters, and attributing those characters to their utterances. Although we find that existing systems can perform the first two tasks accurately, attributing characters to direct speech is a challenging problem due to the narrator{'}s lack of explicit character mentions, and the frequent use of nominal and pronominal coreference when such explicit mentions are made. We adapt the progress made on Dialogue State Tracking to tackle a new problem: attributing speakers to dialogues. This is the first application of deep learning to speaker attribution, and it shows that is possible to overcome the need for the hand-crafted features and rules used in the past. Our full pipeline improves the performance of state-of-the-art models by a relative 50{\%} in F1-score.",
}
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<abstract>We present a complete pipeline to extract characters in a novel and link them to their direct-speech utterances. Our model is divided into three independent components: extracting direct-speech, compiling a list of characters, and attributing those characters to their utterances. Although we find that existing systems can perform the first two tasks accurately, attributing characters to direct speech is a challenging problem due to the narrator’s lack of explicit character mentions, and the frequent use of nominal and pronominal coreference when such explicit mentions are made. We adapt the progress made on Dialogue State Tracking to tackle a new problem: attributing speakers to dialogues. This is the first application of deep learning to speaker attribution, and it shows that is possible to overcome the need for the hand-crafted features and rules used in the past. Our full pipeline improves the performance of state-of-the-art models by a relative 50% in F1-score.</abstract>
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%0 Conference Proceedings
%T What does the sea say to the shore? A BERT based DST style approach for speaker to dialogue attribution in novels
%A Cuesta-Lazaro, Carolina
%A Prasad, Animesh
%A Wood, Trevor
%Y Muresan, Smaranda
%Y Nakov, Preslav
%Y Villavicencio, Aline
%S Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2022
%8 May
%I Association for Computational Linguistics
%C Dublin, Ireland
%F cuesta-lazaro-etal-2022-sea
%X We present a complete pipeline to extract characters in a novel and link them to their direct-speech utterances. Our model is divided into three independent components: extracting direct-speech, compiling a list of characters, and attributing those characters to their utterances. Although we find that existing systems can perform the first two tasks accurately, attributing characters to direct speech is a challenging problem due to the narrator’s lack of explicit character mentions, and the frequent use of nominal and pronominal coreference when such explicit mentions are made. We adapt the progress made on Dialogue State Tracking to tackle a new problem: attributing speakers to dialogues. This is the first application of deep learning to speaker attribution, and it shows that is possible to overcome the need for the hand-crafted features and rules used in the past. Our full pipeline improves the performance of state-of-the-art models by a relative 50% in F1-score.
%R 10.18653/v1/2022.acl-long.400
%U https://aclanthology.org/2022.acl-long.400
%U https://doi.org/10.18653/v1/2022.acl-long.400
%P 5820-5829
Markdown (Informal)
[What does the sea say to the shore? A BERT based DST style approach for speaker to dialogue attribution in novels](https://aclanthology.org/2022.acl-long.400) (Cuesta-Lazaro et al., ACL 2022)
ACL