@inproceedings{sen-yilmaz-2020-uncertainty,
title = "Uncertainty and Traffic-Aware Active Learning for Semantic Parsing",
author = "Sen, Priyanka and
Yilmaz, Emine",
editor = "Bogin, Ben and
Iyer, Srinivasan and
Lin, Xi Victoria and
Radev, Dragomir and
Suhr, Alane and
{Panupong} and
Xiong, Caiming and
Yin, Pengcheng and
Yu, Tao and
Zhang, Rui and
Zhong, Victor",
booktitle = "Proceedings of the First Workshop on Interactive and Executable Semantic Parsing",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.intexsempar-1.2",
doi = "10.18653/v1/2020.intexsempar-1.2",
pages = "12--17",
abstract = "Collecting training data for semantic parsing is a time-consuming and expensive task. As a result, there is growing interest in industry to reduce the number of annotations required to train a semantic parser, both to cut down on costs and to limit customer data handled by annotators. In this paper, we propose uncertainty and traffic-aware active learning, a novel active learning method that uses model confidence and utterance frequencies from customer traffic to select utterances for annotation. We show that our method significantly outperforms baselines on an internal customer dataset and the Facebook Task Oriented Parsing (TOP) dataset. On our internal dataset, our method achieves the same accuracy as random sampling with 2,000 fewer annotations.",
}
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<abstract>Collecting training data for semantic parsing is a time-consuming and expensive task. As a result, there is growing interest in industry to reduce the number of annotations required to train a semantic parser, both to cut down on costs and to limit customer data handled by annotators. In this paper, we propose uncertainty and traffic-aware active learning, a novel active learning method that uses model confidence and utterance frequencies from customer traffic to select utterances for annotation. We show that our method significantly outperforms baselines on an internal customer dataset and the Facebook Task Oriented Parsing (TOP) dataset. On our internal dataset, our method achieves the same accuracy as random sampling with 2,000 fewer annotations.</abstract>
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%0 Conference Proceedings
%T Uncertainty and Traffic-Aware Active Learning for Semantic Parsing
%A Sen, Priyanka
%A Yilmaz, Emine
%Y Bogin, Ben
%Y Iyer, Srinivasan
%Y Lin, Xi Victoria
%Y Radev, Dragomir
%Y Suhr, Alane
%Y Xiong, Caiming
%Y Yin, Pengcheng
%Y Yu, Tao
%Y Zhang, Rui
%Y Zhong, Victor
%E Panupong
%S Proceedings of the First Workshop on Interactive and Executable Semantic Parsing
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F sen-yilmaz-2020-uncertainty
%X Collecting training data for semantic parsing is a time-consuming and expensive task. As a result, there is growing interest in industry to reduce the number of annotations required to train a semantic parser, both to cut down on costs and to limit customer data handled by annotators. In this paper, we propose uncertainty and traffic-aware active learning, a novel active learning method that uses model confidence and utterance frequencies from customer traffic to select utterances for annotation. We show that our method significantly outperforms baselines on an internal customer dataset and the Facebook Task Oriented Parsing (TOP) dataset. On our internal dataset, our method achieves the same accuracy as random sampling with 2,000 fewer annotations.
%R 10.18653/v1/2020.intexsempar-1.2
%U https://aclanthology.org/2020.intexsempar-1.2
%U https://doi.org/10.18653/v1/2020.intexsempar-1.2
%P 12-17
Markdown (Informal)
[Uncertainty and Traffic-Aware Active Learning for Semantic Parsing](https://aclanthology.org/2020.intexsempar-1.2) (Sen & Yilmaz, intexsempar 2020)
ACL