Everything Is All It Takes: A Multipronged Strategy for Zero-Shot Cross-Lingual Information Extraction

Yarmohammadi, Mahsa; Wu, Shaohua; Marone, Marc; Xu, Hang; Ebner, Seth; Qin, Guanghui; Chen, Yunmo; Guo, Jialiang; Harman, Craig; Murray, Kenton; White, Aaron Steven; Dredze, Mark; Durme, Benjamin Van

doi:10.48550/arxiv.2109.06798

Cited by 2 publications

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“…When moving to new types, domains or languages, we have to start from scratch by creating annotations and re-training the extraction models. In this part of tutorial, we will cover the recent advances in improving the transferability of IE, including (1) cross-lingual transfer by leveraging adversarial training (Chen et al, 2019a;Zhou et al, 2019), language-invariant representations (Huang et al, 2018a; and resources (Tsai et al, 2016;Pan et al, 2017), pre-trained multilingual language models (Wu and Dredze, 2019;Conneau et al, 2020) as well as data projection (Ni et al, 2017;Yarmohammadi et al, 2021), (2) cross-type transfer including zero-shot and few-shot IE by learning prototypes (Huang et al, 2018b;Chan et al, 2019;, reading the definitions (Chen et al, 2020b;Logeswaran et al, 2019;Obeidat et al, 2019;Yu et al, 2022;Wang et al, 2022a), answering questions (Levy et al, 2017;Liu et al, 2020;Lyu et al, 2021), and (3) transfer across different benchmark datasets (Xia and Van Durme, 2021;Wang et al, 2021b). Finally, we will also discuss the progress on life-long learning for IE Cao et al, 2020;Yu et al, 2021;Liu et al, 2022) to enable knowledge transfer across incrementally updated models.…”

Section: Transferablity Of Ie Systems [35min]mentioning

confidence: 99%

New Frontiers of Information Extraction

Chen¹,

Huang²,

Li³

et al. 2022

Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Langua

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This tutorial targets researchers and practitioners who are interested in AI and ML technologies for structural information extraction (IE) from unstructured textual sources. In particular, this tutorial will provide audience with a systematic introduction to recent advances in IE, by addressing several important research questions. These questions include (i) how to develop a robust IE system from a small amount of noisy training data, while ensuring the reliability of its prediction? (ii) how to foster the generalizability of IE through enhancing the system's cross-lingual, crossdomain, cross-task and cross-modal transferability? (iii) how to support extracting structural information with extremely fine-grained and diverse labels? (iv) how to further improve IE by leveraging indirect supervision from other NLP tasks, such as Natural Language Generation (NLG), Natural Language Inference (NLI), Question Answering (QA) or summarization, and pre-trained language models? (v) how to acquire knowledge to guide inference in 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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Section: Transferablity Of Ie Systems [35min]mentioning

confidence: 99%

New Frontiers of Information Extraction

Chen¹,

Huang²,

Li³

et al. 2022

Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Langua

View full text Add to dashboard Cite

show abstract

“…When moving to new types, domains or languages, we have to start from scratch by creating annotations and re-training the extraction models. In this part of tutorial, we will cover the recent advances in improving the transferability of IE, including (1) cross-lingual transfer by leveraging adversarial training (Chen et al, 2019a;Huang et al, 2019;Zhou et al, 2019), language-invariant representations (Huang et al, 2018a;Subburathinam et al, 2019) and resources (Tsai et al, 2016;Pan et al, 2017), pre-trained multilingual language models (Wu and Dredze, 2019;Conneau et al, 2020) as well as data projection (Ni et al, 2017;Yarmohammadi et al, 2021), (2) cross-type transfer including zero-shot and few-shot IE by learning prototypes (Huang et al, 2018b;Chan et al, 2019;Huang and Ji, 2020), reading the definitions (Chen et al, 2020b;Logeswaran et al, 2019;Obeidat et al, 2019;Yu et al, 2022;Wang et al, 2022a), answering questions (Levy et al, 2017;Lyu et al, 2021), and (3) transfer across different benchmark datasets (Xia and Van Durme, 2021;. Finally, we will also discuss the progress on life-long learning for IE Cao et al, 2020;Yu et al, 2021;Liu et al, 2022) to enable knowledge transfer across incrementally updated models.…”

Section: Transferablity Of Ie Systems [35min]mentioning

confidence: 99%

Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Tutorial Abstracts

2022

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Text-editing models have recently become a prominent alternative to seq2seq models for monolingual text-generation tasks such as grammatical error correction, simplification, and style transfer. These tasks share a common trait -they exhibit a large amount of textual overlap between the source and target texts. Text-editing models take advantage of this observation and learn to generate the output by predicting edit operations applied to the source sequence. In contrast, seq2seq models generate outputs word-by-word from scratch thus making them slow at inference time. Text-editing models provide several benefits over seq2seq models including faster inference speed, higher sample efficiency, and better control and interpretability of the outputs. This tutorial 1 provides a comprehensive overview of text-editing models and current state-of-the-art approaches, and analyzes their pros and cons. We discuss challenges related to productionization and how these models can be used to mitigate hallucination and bias, both pressing challenges in the field of text generation.

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Everything Is All It Takes: A Multipronged Strategy for Zero-Shot Cross-Lingual Information Extraction

Cited by 2 publications

References 0 publications

New Frontiers of Information Extraction

New Frontiers of Information Extraction

Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Tutorial Abstracts

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