@inproceedings{chen-etal-2020-finding,
title = "Finding {F}riends and Flipping Frenemies: Automatic Paraphrase Dataset Augmentation Using Graph Theory",
author = "Chen, Hannah and
Ji, Yangfeng and
Evans, David",
editor = "Cohn, Trevor and
He, Yulan and
Liu, Yang",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.findings-emnlp.426",
doi = "10.18653/v1/2020.findings-emnlp.426",
pages = "4741--4751",
abstract = "Most NLP datasets are manually labeled, so suffer from inconsistent labeling or limited size. We propose methods for automatically improving datasets by viewing them as graphs with expected semantic properties. We construct a paraphrase graph from the provided sentence pair labels, and create an augmented dataset by directly inferring labels from the original sentence pairs using a transitivity property. We use structural balance theory to identify likely mislabelings in the graph, and flip their labels. We evaluate our methods on paraphrase models trained using these datasets starting from a pretrained BERT model, and find that the automatically-enhanced training sets result in more accurate models.",
}
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%0 Conference Proceedings
%T Finding Friends and Flipping Frenemies: Automatic Paraphrase Dataset Augmentation Using Graph Theory
%A Chen, Hannah
%A Ji, Yangfeng
%A Evans, David
%Y Cohn, Trevor
%Y He, Yulan
%Y Liu, Yang
%S Findings of the Association for Computational Linguistics: EMNLP 2020
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F chen-etal-2020-finding
%X Most NLP datasets are manually labeled, so suffer from inconsistent labeling or limited size. We propose methods for automatically improving datasets by viewing them as graphs with expected semantic properties. We construct a paraphrase graph from the provided sentence pair labels, and create an augmented dataset by directly inferring labels from the original sentence pairs using a transitivity property. We use structural balance theory to identify likely mislabelings in the graph, and flip their labels. We evaluate our methods on paraphrase models trained using these datasets starting from a pretrained BERT model, and find that the automatically-enhanced training sets result in more accurate models.
%R 10.18653/v1/2020.findings-emnlp.426
%U https://aclanthology.org/2020.findings-emnlp.426
%U https://doi.org/10.18653/v1/2020.findings-emnlp.426
%P 4741-4751
Markdown (Informal)
[Finding Friends and Flipping Frenemies: Automatic Paraphrase Dataset Augmentation Using Graph Theory](https://aclanthology.org/2020.findings-emnlp.426) (Chen et al., Findings 2020)
ACL