@inproceedings{wu-etal-2022-context,
title = "Context-aware Information-theoretic Causal De-biasing for Interactive Sequence Labeling",
author = "Wu, Junda and
Wang, Rui and
Yu, Tong and
Zhang, Ruiyi and
Zhao, Handong and
Li, Shuai and
Henao, Ricardo and
Nenkova, Ani",
editor = "Goldberg, Yoav and
Kozareva, Zornitsa and
Zhang, Yue",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2022",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.findings-emnlp.251",
doi = "10.18653/v1/2022.findings-emnlp.251",
pages = "3436--3448",
abstract = "Supervised training of existing deep learning models for sequence labeling relies on large scale labeled datasets. Such datasets are generally created with crowd-source labeling. However, crowd-source labeling for tasks of sequence labeling can be expensive and time-consuming. Further, crowd-source labeling by external annotators may not be appropriate for data that contains user private information. Considering the above limitations of crowd-source labeling, we study interactive sequence labeling that allows training directly with the user feedback, which alleviates the annotation cost and maintains the user privacy. We identify two bias, namely, context bias and feedback bias, by formulating interactive sequence labeling via a Structural Causal Model (SCM). To alleviate the context and feedback bias based on the SCM, we identify the frequent context tokens as confounders in the backdoor adjustment and further propose an entropy-based modulation that is inspired by information theory. entities more sample-efficiently. With extensive experiments, we validate that our approach can effectively alleviate the biases and our models can be efficiently learnt with the user feedback.",
}
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<abstract>Supervised training of existing deep learning models for sequence labeling relies on large scale labeled datasets. Such datasets are generally created with crowd-source labeling. However, crowd-source labeling for tasks of sequence labeling can be expensive and time-consuming. Further, crowd-source labeling by external annotators may not be appropriate for data that contains user private information. Considering the above limitations of crowd-source labeling, we study interactive sequence labeling that allows training directly with the user feedback, which alleviates the annotation cost and maintains the user privacy. We identify two bias, namely, context bias and feedback bias, by formulating interactive sequence labeling via a Structural Causal Model (SCM). To alleviate the context and feedback bias based on the SCM, we identify the frequent context tokens as confounders in the backdoor adjustment and further propose an entropy-based modulation that is inspired by information theory. entities more sample-efficiently. With extensive experiments, we validate that our approach can effectively alleviate the biases and our models can be efficiently learnt with the user feedback.</abstract>
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%0 Conference Proceedings
%T Context-aware Information-theoretic Causal De-biasing for Interactive Sequence Labeling
%A Wu, Junda
%A Wang, Rui
%A Yu, Tong
%A Zhang, Ruiyi
%A Zhao, Handong
%A Li, Shuai
%A Henao, Ricardo
%A Nenkova, Ani
%Y Goldberg, Yoav
%Y Kozareva, Zornitsa
%Y Zhang, Yue
%S Findings of the Association for Computational Linguistics: EMNLP 2022
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, United Arab Emirates
%F wu-etal-2022-context
%X Supervised training of existing deep learning models for sequence labeling relies on large scale labeled datasets. Such datasets are generally created with crowd-source labeling. However, crowd-source labeling for tasks of sequence labeling can be expensive and time-consuming. Further, crowd-source labeling by external annotators may not be appropriate for data that contains user private information. Considering the above limitations of crowd-source labeling, we study interactive sequence labeling that allows training directly with the user feedback, which alleviates the annotation cost and maintains the user privacy. We identify two bias, namely, context bias and feedback bias, by formulating interactive sequence labeling via a Structural Causal Model (SCM). To alleviate the context and feedback bias based on the SCM, we identify the frequent context tokens as confounders in the backdoor adjustment and further propose an entropy-based modulation that is inspired by information theory. entities more sample-efficiently. With extensive experiments, we validate that our approach can effectively alleviate the biases and our models can be efficiently learnt with the user feedback.
%R 10.18653/v1/2022.findings-emnlp.251
%U https://aclanthology.org/2022.findings-emnlp.251
%U https://doi.org/10.18653/v1/2022.findings-emnlp.251
%P 3436-3448
Markdown (Informal)
[Context-aware Information-theoretic Causal De-biasing for Interactive Sequence Labeling](https://aclanthology.org/2022.findings-emnlp.251) (Wu et al., Findings 2022)
ACL
- Junda Wu, Rui Wang, Tong Yu, Ruiyi Zhang, Handong Zhao, Shuai Li, Ricardo Henao, and Ani Nenkova. 2022. Context-aware Information-theoretic Causal De-biasing for Interactive Sequence Labeling. In Findings of the Association for Computational Linguistics: EMNLP 2022, pages 3436–3448, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.