@inproceedings{knaebel-stede-2020-contextualized,
title = "Contextualized Embeddings for Connective Disambiguation in Shallow Discourse Parsing",
author = "Knaebel, Ren{\'e} and
Stede, Manfred",
editor = "Braud, Chlo{\'e} and
Hardmeier, Christian and
Li, Junyi Jessy and
Louis, Annie and
Strube, Michael",
booktitle = "Proceedings of the First Workshop on Computational Approaches to Discourse",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.codi-1.7",
doi = "10.18653/v1/2020.codi-1.7",
pages = "65--75",
abstract = "This paper studies a novel model that simplifies the disambiguation of connectives for explicit discourse relations. We use a neural approach that integrates contextualized word embeddings and predicts whether a connective candidate is part of a discourse relation or not. We study the influence of those context-specific embeddings. Further, we show the benefit of training the tasks of connective disambiguation and sense classification together at the same time. The success of our approach is supported by state-of-the-art results.",
}
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%0 Conference Proceedings
%T Contextualized Embeddings for Connective Disambiguation in Shallow Discourse Parsing
%A Knaebel, René
%A Stede, Manfred
%Y Braud, Chloé
%Y Hardmeier, Christian
%Y Li, Junyi Jessy
%Y Louis, Annie
%Y Strube, Michael
%S Proceedings of the First Workshop on Computational Approaches to Discourse
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F knaebel-stede-2020-contextualized
%X This paper studies a novel model that simplifies the disambiguation of connectives for explicit discourse relations. We use a neural approach that integrates contextualized word embeddings and predicts whether a connective candidate is part of a discourse relation or not. We study the influence of those context-specific embeddings. Further, we show the benefit of training the tasks of connective disambiguation and sense classification together at the same time. The success of our approach is supported by state-of-the-art results.
%R 10.18653/v1/2020.codi-1.7
%U https://aclanthology.org/2020.codi-1.7
%U https://doi.org/10.18653/v1/2020.codi-1.7
%P 65-75
Markdown (Informal)
[Contextualized Embeddings for Connective Disambiguation in Shallow Discourse Parsing](https://aclanthology.org/2020.codi-1.7) (Knaebel & Stede, CODI 2020)
ACL