@inproceedings{yao-etal-2023-beyond,
title = "Beyond Labels: Empowering Human Annotators with Natural Language Explanations through a Novel Active-Learning Architecture",
author = "Yao, Bingsheng and
Jindal, Ishan and
Popa, Lucian and
Katsis, Yannis and
Ghosh, Sayan and
He, Lihong and
Lu, Yuxuan and
Srivastava, Shashank and
Li, Yunyao and
Hendler, James and
Wang, Dakuo",
editor = "Bouamor, Houda and
Pino, Juan and
Bali, Kalika",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2023",
month = dec,
year = "2023",
address = "Singapore",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.findings-emnlp.778",
doi = "10.18653/v1/2023.findings-emnlp.778",
pages = "11629--11643",
abstract = "Real-world domain experts (e.g., doctors) rarely annotate only a decision label in their day-to-day workflow without providing explanations. Yet, existing low-resource learning techniques, such as Active Learning (AL), that aim to support human annotators mostly focus on the label while neglecting the natural language explanation of a data point. This work proposes a novel AL architecture to support experts{'} real-world need for label and explanation annotations in low-resource scenarios. Our AL architecture leverages an explanation-generation model to produce explanations guided by human explanations, a prediction model that utilizes generated explanations toward prediction faithfully, and a novel data diversity-based AL sampling strategy that benefits from the explanation annotations. Automated and human evaluations demonstrate the effectiveness of incorporating explanations into AL sampling and the improved human annotation efficiency and trustworthiness with our AL architecture. Additional ablation studies illustrate the potential of our AL architecture for transfer learning, generalizability, and integration with large language models (LLMs). While LLMs exhibit exceptional explanation-generation capabilities for relatively simple tasks, their effectiveness in complex real-world tasks warrants further in-depth study.",
}
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<abstract>Real-world domain experts (e.g., doctors) rarely annotate only a decision label in their day-to-day workflow without providing explanations. Yet, existing low-resource learning techniques, such as Active Learning (AL), that aim to support human annotators mostly focus on the label while neglecting the natural language explanation of a data point. This work proposes a novel AL architecture to support experts’ real-world need for label and explanation annotations in low-resource scenarios. Our AL architecture leverages an explanation-generation model to produce explanations guided by human explanations, a prediction model that utilizes generated explanations toward prediction faithfully, and a novel data diversity-based AL sampling strategy that benefits from the explanation annotations. Automated and human evaluations demonstrate the effectiveness of incorporating explanations into AL sampling and the improved human annotation efficiency and trustworthiness with our AL architecture. Additional ablation studies illustrate the potential of our AL architecture for transfer learning, generalizability, and integration with large language models (LLMs). While LLMs exhibit exceptional explanation-generation capabilities for relatively simple tasks, their effectiveness in complex real-world tasks warrants further in-depth study.</abstract>
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%0 Conference Proceedings
%T Beyond Labels: Empowering Human Annotators with Natural Language Explanations through a Novel Active-Learning Architecture
%A Yao, Bingsheng
%A Jindal, Ishan
%A Popa, Lucian
%A Katsis, Yannis
%A Ghosh, Sayan
%A He, Lihong
%A Lu, Yuxuan
%A Srivastava, Shashank
%A Li, Yunyao
%A Hendler, James
%A Wang, Dakuo
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Findings of the Association for Computational Linguistics: EMNLP 2023
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F yao-etal-2023-beyond
%X Real-world domain experts (e.g., doctors) rarely annotate only a decision label in their day-to-day workflow without providing explanations. Yet, existing low-resource learning techniques, such as Active Learning (AL), that aim to support human annotators mostly focus on the label while neglecting the natural language explanation of a data point. This work proposes a novel AL architecture to support experts’ real-world need for label and explanation annotations in low-resource scenarios. Our AL architecture leverages an explanation-generation model to produce explanations guided by human explanations, a prediction model that utilizes generated explanations toward prediction faithfully, and a novel data diversity-based AL sampling strategy that benefits from the explanation annotations. Automated and human evaluations demonstrate the effectiveness of incorporating explanations into AL sampling and the improved human annotation efficiency and trustworthiness with our AL architecture. Additional ablation studies illustrate the potential of our AL architecture for transfer learning, generalizability, and integration with large language models (LLMs). While LLMs exhibit exceptional explanation-generation capabilities for relatively simple tasks, their effectiveness in complex real-world tasks warrants further in-depth study.
%R 10.18653/v1/2023.findings-emnlp.778
%U https://aclanthology.org/2023.findings-emnlp.778
%U https://doi.org/10.18653/v1/2023.findings-emnlp.778
%P 11629-11643
Markdown (Informal)
[Beyond Labels: Empowering Human Annotators with Natural Language Explanations through a Novel Active-Learning Architecture](https://aclanthology.org/2023.findings-emnlp.778) (Yao et al., Findings 2023)
ACL
- Bingsheng Yao, Ishan Jindal, Lucian Popa, Yannis Katsis, Sayan Ghosh, Lihong He, Yuxuan Lu, Shashank Srivastava, Yunyao Li, James Hendler, and Dakuo Wang. 2023. Beyond Labels: Empowering Human Annotators with Natural Language Explanations through a Novel Active-Learning Architecture. In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 11629–11643, Singapore. Association for Computational Linguistics.