@inproceedings{chen-etal-2022-new,
title = "New Frontiers of Information Extraction",
author = "Chen, Muhao and
Huang, Lifu and
Li, Manling and
Zhou, Ben and
Ji, Heng and
Roth, Dan",
editor = "Ballesteros, Miguel and
Tsvetkov, Yulia and
Alm, Cecilia O.",
booktitle = "Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Tutorial Abstracts",
month = jul,
year = "2022",
address = "Seattle, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.naacl-tutorials.3",
doi = "10.18653/v1/2022.naacl-tutorials.3",
pages = "14--25",
abstract = "This tutorial targets researchers and practitioners who are interested in AI and ML technologies for structural information extraction (IE) from unstructured textual sources. Particularly, this tutorial will provide audience with a systematic introduction to recent advances of IE, by answering several important research questions. These questions include (i) how to develop an robust IE system from noisy, insufficient training data, while ensuring the reliability of its prediction? (ii) how to foster the generalizability of IE through enhancing the system{'}s cross-lingual, cross-domain, cross-task and cross-modal transferability? (iii) how to precisely support extracting structural information with extremely fine-grained, diverse and boundless labels? (iv) how to further improve IE by leveraging indirect supervision from other NLP tasks, such as NLI, QA or summarization, and pre-trained language models? (v) how to acquire knowledge to guide the inference of IE systems? We will discuss several lines of frontier research that tackle those challenges, and will conclude the tutorial by outlining directions for further investigation.",
}
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<abstract>This tutorial targets researchers and practitioners who are interested in AI and ML technologies for structural information extraction (IE) from unstructured textual sources. Particularly, this tutorial will provide audience with a systematic introduction to recent advances of IE, by answering several important research questions. These questions include (i) how to develop an robust IE system from noisy, insufficient training data, while ensuring the reliability of its prediction? (ii) how to foster the generalizability of IE through enhancing the system’s cross-lingual, cross-domain, cross-task and cross-modal transferability? (iii) how to precisely support extracting structural information with extremely fine-grained, diverse and boundless labels? (iv) how to further improve IE by leveraging indirect supervision from other NLP tasks, such as NLI, QA or summarization, and pre-trained language models? (v) how to acquire knowledge to guide the inference of IE systems? We will discuss several lines of frontier research that tackle those challenges, and will conclude the tutorial by outlining directions for further investigation.</abstract>
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%0 Conference Proceedings
%T New Frontiers of Information Extraction
%A Chen, Muhao
%A Huang, Lifu
%A Li, Manling
%A Zhou, Ben
%A Ji, Heng
%A Roth, Dan
%Y Ballesteros, Miguel
%Y Tsvetkov, Yulia
%Y Alm, Cecilia O.
%S Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Tutorial Abstracts
%D 2022
%8 July
%I Association for Computational Linguistics
%C Seattle, United States
%F chen-etal-2022-new
%X This tutorial targets researchers and practitioners who are interested in AI and ML technologies for structural information extraction (IE) from unstructured textual sources. Particularly, this tutorial will provide audience with a systematic introduction to recent advances of IE, by answering several important research questions. These questions include (i) how to develop an robust IE system from noisy, insufficient training data, while ensuring the reliability of its prediction? (ii) how to foster the generalizability of IE through enhancing the system’s cross-lingual, cross-domain, cross-task and cross-modal transferability? (iii) how to precisely support extracting structural information with extremely fine-grained, diverse and boundless labels? (iv) how to further improve IE by leveraging indirect supervision from other NLP tasks, such as NLI, QA or summarization, and pre-trained language models? (v) how to acquire knowledge to guide the inference of IE systems? We will discuss several lines of frontier research that tackle those challenges, and will conclude the tutorial by outlining directions for further investigation.
%R 10.18653/v1/2022.naacl-tutorials.3
%U https://aclanthology.org/2022.naacl-tutorials.3
%U https://doi.org/10.18653/v1/2022.naacl-tutorials.3
%P 14-25
Markdown (Informal)
[New Frontiers of Information Extraction](https://aclanthology.org/2022.naacl-tutorials.3) (Chen et al., NAACL 2022)
ACL
- Muhao Chen, Lifu Huang, Manling Li, Ben Zhou, Heng Ji, and Dan Roth. 2022. New Frontiers of Information Extraction. In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Tutorial Abstracts, pages 14–25, Seattle, United States. Association for Computational Linguistics.