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Re-Tacred: Addressing Shortcomings Of The Tacred Dataset

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This is reasonable as Re-TACRED is an improved version among these three datasets, which addresses some shortcomings of the original TACRED dataset, refactors its training set, development set and test set.

An ablation study of our best model over the TACRED test dataset ...

Dataset Summary MultiTACRED is a multilingual version of the large-scale TAC Relation Extraction Dataset. It covers 12 typologically diverse languages from 9 language families, and was created by the Speech & Language Technology group of DFKI by machine-translating the instances of the original TACRED dataset and automatically projecting their entity annotations.

broader implications of its findings. In this paper, we address these shortcomings by: (i) performing a comprehensive study over the whole TACRED dataset, (ii) proposing an improved crowdsourcing strategy and deploy-ing it to re-annotate the whole dataset, and (iii) performing a thorough analysis to understand how correcting the TACRED annotation In this paper, we address these shortcomings by: (i) performing a comprehensive study over the whole TACRED dataset, (ii) proposing an improved crowdsourcing strategy and deploying it to re-annotate the whole dataset, and (iii) performing a thorough analysis to understand how correcting the TACRED annotations affects previously published results.

dblp: Re-TACRED: Addressing Shortcomings of the TACRED Dataset.

Introduction TACRED is a large-scale relation extraction dataset with 106,264 examples built over newswire and web text from the corpus used in the yearly TAC Knowledge Base Population (TAC KBP) challenges. Examples in TACRED cover 41 relation types as used in the TAC KBP challenges (e.g., per:schools_attended and org:members) or are labeled as no_relation if no

In this paper, we address these shortcomings by: (i) performing a comprehensive study over the whole TACRED dataset, (ii) proposing an improved crowdsourcing strategy and deploy- ing it to re-annotate the whole dataset, and (iii) performing a thorough analysis tounderstand how correcting the TACRED annotations affects previously published results. broader implications of its findings. In this paper, we address these shortcomings by: (i) performing a comprehensive study over the whole TACRED dataset, (ii) proposing an improved crowdsourcing strategy and deploy-ing it to re-annotate the whole dataset, and (iii) performing a thorough analysis to understand how correcting the TACRED annotation Re-TACRED, a new completely re-annotated version of the TACRED dataset that can be used to perform reliable evaluation of relation extraction models, is released.

Finally, aside from our analysis we also release Re-TACRED, a new completely re-annotated version of the TACRED dataset that can be used to perform reliable evaluation of relation extraction models. In this paper, we address these shortcomings by: (i) performing a comprehensive study over the whole TACRED dataset, (ii) proposing an improved crowdsourcing strategy and deploy-ing it to re-annotate the whole dataset, and (iii) performing a thorough analysis to understand how correcting the TACRED annotations affects previously published results. In this paper, we address these shortcomings by: (i) performing a comprehensive study over the whole TACRED dataset, (ii) proposing an improved crowdsourcing strategy and deploying it to re-annotate the whole dataset, and (iii) performing a thorough analysis to understand how correcting the TACRED annotations affects previously published results.

In this paper, we address these shortcomings by: (i) performing a comprehensive study over the whole TACRED dataset, (ii) proposing an improved crowdsourcing strategy and deploy-ing it to re-annotate the whole dataset, and (iii) performing a thorough analysis to understand how correcting the TACRED annotations affects previously In this paper, we address these shortcomings by: (i) performing a comprehensive study over the whole TACRED dataset, (ii) proposing an improved crowdsourcing strategy and deploying it to re-annotate the whole dataset, and (iii) performing a thorough analysis to understand how correcting the TACRED annotations affects previously

Bibliographic details on Re-TACRED: Addressing Shortcomings of the TACRED Dataset. red to the TACRED and TACREV datasets. This is reasonable as Re-TACRED is an improved version among these three datasets, which addresses some shortcomings of the original TACRED dataset, refactors its trai In this paper, we address these shortcomings by: (i) performing a comprehensive study over the whole TACRED dataset, (ii) proposing an improved crowdsourcing strategy and deploying it to re-annotate the whole dataset, and (iii) performing a thorough analysis to understand how correcting the TACRED annotations affects previously published results.

Re-TACRED: Addressing Shortcomings of the TACRED Dataset.

In this paper, we address these shortcomings by: (i) performing a comprehensive study over the whole TACRED dataset, (ii) proposing an improved crowdsourcing strategy and deploying it to re-annotate the whole dataset, and (iii) performing a thorough analysis to understand how correcting the TACRED annotations affects previously published results. Barnab {\‘ {a}}s P {\‘ {o}}czos}, title = {Re-TACRED: Addressing Shortcomings of the {TACRED} Dataset}, booktitle = {Thirty-Fifth {AAAI} Conference on Artificial Intelligence, {AAAI} 2021, Thirty-Third Conference on Innovative Applications of Artificial Intelligence, {IAAI} 2021, The Eleventh Symposium on Educational Advances

REFinD

This is reasonable as Re-TACRED is an improved version among these three datasets, which addresses some shortcomings of the original TACRED dataset, refactors its training set, development set and test set.

In this paper, we address these shortcomings by: (i) performing a comprehensive study over the whole TACRED dataset, (ii) proposing an improved crowdsourcing strategy and deploying it to re-annotate the whole dataset, and (iii) performing a thorough analysis to understand how correcting the TACRED annotations affects previously This is reasonable as Re-TACRED is an improved version among these three datasets, which addresses some shortcomings of the original TACRED dataset, refactors its training set, development set and test set. Re-TACRED数据集是自然语言处理领域中的一项重要资源，由斯坦福大学和微软研究院的研究团队于2020年共同创建。该数据集基于原始的TACRED数据集，旨在解决关系抽取任务中的标注不一致性和类别不平衡问题。Re-TACRED通过重新标注和调整，提供了更为准确和平衡的训练数据，显著提升了模型的性能

有关这个新版本的更多详细信息，请参见2020年ACL上发布的 Re-TACRED paper 。 此存储库提供了数据集的所有三个版本作为BuilderConfigs-‚original’，’revisited’和’re-tacred’。 只需在load_dataset方法中设置name参数，即可选择特定版本。 默认情况下加载原始TACRED。 支持的

Re-TACRED: Addressing Shortcomings of the TACRED Dataset. In Thirty-Fifth AAAI Conference on Artificial Intelligence, AAAI 2021, Thirty-Third Conference on Innovative Applications of Artificial Intelligence, IAAI 2021, The Eleventh Symposium on Educational Advances in Artificial Intelligence, EAAI 2021, Virtual Event, February 2-9, 2021. pages Bibliographic details on Re-TACRED: Addressing Shortcomings of the TACRED Dataset. Re-TACRED: Addressing Shortcomings of the TACRED Dataset George Stoica, Emmanouil Antonios Platanios, and Barnabás Póczos Proceedings of the AAAI Conference on Artificial Intelligence 2021 PDF

README.md · DFKI-SLT/tacred at main

PER stands for PERSON and ORG for ORGANIZATION. „*“ indicates all object types. from publication: Re-TACRED: Addressing Shortcomings of the TACRED Dataset | TACRED is one of the largest and most

[14] G. Stoica, E. A. Platanios, and B. Póczos, “Re-tacred: Addressing shortcomings of the tacred dataset,” in Proceedings of the Thirty-fifth AAAI Conference on Aritificial Intelligence, 2021. The blue social bookmark and publication sharing system.

broader implications of its findings. In this paper, we address these shortcomings by: (i) performing a comprehensive study over the whole TACRED dataset, (ii) proposing an improved crowdsourcing strategy and deploy-ing it to re-annotate the whole dataset, and (iii) performing a thorough analysis to understand how correcting the TACRED annotation Re-TACRED 数据集是用于关系提取的 TACRED 数据集的显着改进版本。 使用新的众包标签，Re-TACRED 修剪了注释不佳的句子并解决了 TACRED 关系定义的歧义，最终纠正了 23.9% 的 TACRED 标签。 该数据集包含分布在 40 个关系中的超过 91,000 个句子。 在 AAAI 2021 上展示的

In this paper, we address these shortcomings by: (i) performing a comprehensive study over the whole TACRED dataset, (ii) proposing an improved crowdsourcing strategy and deploying it to re-annotate the whole dataset, and (iii) performing a thorough analysis to understand how correcting the TACRED annotations affects previously published results. In this paper, we address these shortcomings by: (i) performing a comprehensive study over the whole TACRED dataset, (ii) proposing an improved crowdsourcing strategy and deploying it to re-annotate the whole dataset, and (iii) performing a thorough analysis to understand how correcting the TACRED annotations affects previously published results. Re-TACRED: Addressing Shortcomings of the TACRED Dataset TACRED is one of the largest and most widely used sentence-level relatio

Article „Re-TACRED: Addressing Shortcomings of the TACRED Dataset“ Detailed information of the J-GLOBAL is an information service managed by the Japan Science and Technology Agency (hereinafter referred to as „JST“). It provides free access to secondary information on researchers, articles, patents, etc., in science and technology, medicine and pharmacy. The search results

George Stoica, Emmanouil Antonios Platanios, and Barnabas Poczos. 2021.Re-tacred: Addressing shortcomings of the tacred dataset. Proceedings of the AAAI Conference on Articial Intelligence , 35(15):13843 13850.

In this paper, we address these shortcomings by performing a comprehensive re-annotation of the entire TACRED dataset. Our contributions can be summarized as follows. First, we deploy an improved and cost-efficient crowd-sourcing annotation strategy over the dataset. Our annotations achieve an average agreement rate of 82.3% and inter-annotator Fleiss’ kappa of .77 ‪Georgia Institute of Technology and University of Washington‬ – ‪‪Cited by 372‬‬ – ‪Machine Learning‬