@inproceedings{anand-etal-2020-midas,
title = "{MIDAS} at {S}em{E}val-2020 Task 10: Emphasis Selection Using Label Distribution Learning and Contextual Embeddings",
author = "Anand, Sarthak and
Gupta, Pradyumna and
Yadav, Hemant and
Mahata, Debanjan and
Gosangi, Rakesh and
Zhang, Haimin and
Shah, Rajiv Ratn",
editor = "Herbelot, Aurelie and
Zhu, Xiaodan and
Palmer, Alexis and
Schneider, Nathan and
May, Jonathan and
Shutova, Ekaterina",
booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
month = dec,
year = "2020",
address = "Barcelona (online)",
publisher = "International Committee for Computational Linguistics",
url = "https://aclanthology.org/2020.semeval-1.219",
doi = "10.18653/v1/2020.semeval-1.219",
pages = "1678--1684",
abstract = "This paper presents our submission to the SemEval 2020 - Task 10 on emphasis selection in written text. We approach this emphasis selection problem as a sequence labeling task where we represent the underlying text with various contextual embedding models. We also employ label distribution learning to account for annotator disagreements. We experiment with the choice of model architectures, trainability of layers, and different contextual embeddings. Our best performing architecture is an ensemble of different models, which achieved an overall matching score of 0.783, placing us 15th out of 31 participating teams. Lastly, we analyze the results in terms of parts of speech tags, sentence lengths, and word ordering.",
}
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<abstract>This paper presents our submission to the SemEval 2020 - Task 10 on emphasis selection in written text. We approach this emphasis selection problem as a sequence labeling task where we represent the underlying text with various contextual embedding models. We also employ label distribution learning to account for annotator disagreements. We experiment with the choice of model architectures, trainability of layers, and different contextual embeddings. Our best performing architecture is an ensemble of different models, which achieved an overall matching score of 0.783, placing us 15th out of 31 participating teams. Lastly, we analyze the results in terms of parts of speech tags, sentence lengths, and word ordering.</abstract>
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%0 Conference Proceedings
%T MIDAS at SemEval-2020 Task 10: Emphasis Selection Using Label Distribution Learning and Contextual Embeddings
%A Anand, Sarthak
%A Gupta, Pradyumna
%A Yadav, Hemant
%A Mahata, Debanjan
%A Gosangi, Rakesh
%A Zhang, Haimin
%A Shah, Rajiv Ratn
%Y Herbelot, Aurelie
%Y Zhu, Xiaodan
%Y Palmer, Alexis
%Y Schneider, Nathan
%Y May, Jonathan
%Y Shutova, Ekaterina
%S Proceedings of the Fourteenth Workshop on Semantic Evaluation
%D 2020
%8 December
%I International Committee for Computational Linguistics
%C Barcelona (online)
%F anand-etal-2020-midas
%X This paper presents our submission to the SemEval 2020 - Task 10 on emphasis selection in written text. We approach this emphasis selection problem as a sequence labeling task where we represent the underlying text with various contextual embedding models. We also employ label distribution learning to account for annotator disagreements. We experiment with the choice of model architectures, trainability of layers, and different contextual embeddings. Our best performing architecture is an ensemble of different models, which achieved an overall matching score of 0.783, placing us 15th out of 31 participating teams. Lastly, we analyze the results in terms of parts of speech tags, sentence lengths, and word ordering.
%R 10.18653/v1/2020.semeval-1.219
%U https://aclanthology.org/2020.semeval-1.219
%U https://doi.org/10.18653/v1/2020.semeval-1.219
%P 1678-1684
Markdown (Informal)
[MIDAS at SemEval-2020 Task 10: Emphasis Selection Using Label Distribution Learning and Contextual Embeddings](https://aclanthology.org/2020.semeval-1.219) (Anand et al., SemEval 2020)
ACL