@inproceedings{toledo-etal-2019-automatic,
title = "Automatic Argument Quality Assessment - New Datasets and Methods",
author = "Toledo, Assaf and
Gretz, Shai and
Cohen-Karlik, Edo and
Friedman, Roni and
Venezian, Elad and
Lahav, Dan and
Jacovi, Michal and
Aharonov, Ranit and
Slonim, Noam",
editor = "Inui, Kentaro and
Jiang, Jing and
Ng, Vincent and
Wan, Xiaojun",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
month = nov,
year = "2019",
address = "Hong Kong, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D19-1564",
doi = "10.18653/v1/D19-1564",
pages = "5625--5635",
abstract = "We explore the task of automatic assessment of argument quality. To that end, we actively collected 6.3k arguments, more than a factor of five compared to previously examined data. Each argument was explicitly and carefully annotated for its quality. In addition, 14k pairs of arguments were annotated independently, identifying the higher quality argument in each pair. In spite of the inherent subjective nature of the task, both annotation schemes led to surprisingly consistent results. We release the labeled datasets to the community. Furthermore, we suggest neural methods based on a recently released language model, for argument ranking as well as for argument-pair classification. In the former task, our results are comparable to state-of-the-art; in the latter task our results significantly outperform earlier methods.",
}
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<abstract>We explore the task of automatic assessment of argument quality. To that end, we actively collected 6.3k arguments, more than a factor of five compared to previously examined data. Each argument was explicitly and carefully annotated for its quality. In addition, 14k pairs of arguments were annotated independently, identifying the higher quality argument in each pair. In spite of the inherent subjective nature of the task, both annotation schemes led to surprisingly consistent results. We release the labeled datasets to the community. Furthermore, we suggest neural methods based on a recently released language model, for argument ranking as well as for argument-pair classification. In the former task, our results are comparable to state-of-the-art; in the latter task our results significantly outperform earlier methods.</abstract>
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%0 Conference Proceedings
%T Automatic Argument Quality Assessment - New Datasets and Methods
%A Toledo, Assaf
%A Gretz, Shai
%A Cohen-Karlik, Edo
%A Friedman, Roni
%A Venezian, Elad
%A Lahav, Dan
%A Jacovi, Michal
%A Aharonov, Ranit
%A Slonim, Noam
%Y Inui, Kentaro
%Y Jiang, Jing
%Y Ng, Vincent
%Y Wan, Xiaojun
%S Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
%D 2019
%8 November
%I Association for Computational Linguistics
%C Hong Kong, China
%F toledo-etal-2019-automatic
%X We explore the task of automatic assessment of argument quality. To that end, we actively collected 6.3k arguments, more than a factor of five compared to previously examined data. Each argument was explicitly and carefully annotated for its quality. In addition, 14k pairs of arguments were annotated independently, identifying the higher quality argument in each pair. In spite of the inherent subjective nature of the task, both annotation schemes led to surprisingly consistent results. We release the labeled datasets to the community. Furthermore, we suggest neural methods based on a recently released language model, for argument ranking as well as for argument-pair classification. In the former task, our results are comparable to state-of-the-art; in the latter task our results significantly outperform earlier methods.
%R 10.18653/v1/D19-1564
%U https://aclanthology.org/D19-1564
%U https://doi.org/10.18653/v1/D19-1564
%P 5625-5635
Markdown (Informal)
[Automatic Argument Quality Assessment - New Datasets and Methods](https://aclanthology.org/D19-1564) (Toledo et al., EMNLP-IJCNLP 2019)
ACL
- Assaf Toledo, Shai Gretz, Edo Cohen-Karlik, Roni Friedman, Elad Venezian, Dan Lahav, Michal Jacovi, Ranit Aharonov, and Noam Slonim. 2019. Automatic Argument Quality Assessment - New Datasets and Methods. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 5625–5635, Hong Kong, China. Association for Computational Linguistics.