@inproceedings{ballesteros-etal-2020-severing,
title = "Severing the Edge Between Before and After: Neural Architectures for Temporal Ordering of Events",
author = "Ballesteros, Miguel and
Anubhai, Rishita and
Wang, Shuai and
Pourdamghani, Nima and
Vyas, Yogarshi and
Ma, Jie and
Bhatia, Parminder and
McKeown, Kathleen and
Al-Onaizan, Yaser",
editor = "Webber, Bonnie and
Cohn, Trevor and
He, Yulan and
Liu, Yang",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.emnlp-main.436",
doi = "10.18653/v1/2020.emnlp-main.436",
pages = "5412--5417",
abstract = "In this paper, we propose a neural architecture and a set of training methods for ordering events by predicting temporal relations. Our proposed models receive a pair of events within a span of text as input and they identify temporal relations (Before, After, Equal, Vague) between them. Given that a key challenge with this task is the scarcity of annotated data, our models rely on either pretrained representations (i.e. RoBERTa, BERT or ELMo), transfer and multi-task learning (by leveraging complementary datasets), and self-training techniques. Experiments on the MATRES dataset of English documents establish a new state-of-the-art on this task.",
}
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<abstract>In this paper, we propose a neural architecture and a set of training methods for ordering events by predicting temporal relations. Our proposed models receive a pair of events within a span of text as input and they identify temporal relations (Before, After, Equal, Vague) between them. Given that a key challenge with this task is the scarcity of annotated data, our models rely on either pretrained representations (i.e. RoBERTa, BERT or ELMo), transfer and multi-task learning (by leveraging complementary datasets), and self-training techniques. Experiments on the MATRES dataset of English documents establish a new state-of-the-art on this task.</abstract>
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%0 Conference Proceedings
%T Severing the Edge Between Before and After: Neural Architectures for Temporal Ordering of Events
%A Ballesteros, Miguel
%A Anubhai, Rishita
%A Wang, Shuai
%A Pourdamghani, Nima
%A Vyas, Yogarshi
%A Ma, Jie
%A Bhatia, Parminder
%A McKeown, Kathleen
%A Al-Onaizan, Yaser
%Y Webber, Bonnie
%Y Cohn, Trevor
%Y He, Yulan
%Y Liu, Yang
%S Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F ballesteros-etal-2020-severing
%X In this paper, we propose a neural architecture and a set of training methods for ordering events by predicting temporal relations. Our proposed models receive a pair of events within a span of text as input and they identify temporal relations (Before, After, Equal, Vague) between them. Given that a key challenge with this task is the scarcity of annotated data, our models rely on either pretrained representations (i.e. RoBERTa, BERT or ELMo), transfer and multi-task learning (by leveraging complementary datasets), and self-training techniques. Experiments on the MATRES dataset of English documents establish a new state-of-the-art on this task.
%R 10.18653/v1/2020.emnlp-main.436
%U https://aclanthology.org/2020.emnlp-main.436
%U https://doi.org/10.18653/v1/2020.emnlp-main.436
%P 5412-5417
Markdown (Informal)
[Severing the Edge Between Before and After: Neural Architectures for Temporal Ordering of Events](https://aclanthology.org/2020.emnlp-main.436) (Ballesteros et al., EMNLP 2020)
ACL
- Miguel Ballesteros, Rishita Anubhai, Shuai Wang, Nima Pourdamghani, Yogarshi Vyas, Jie Ma, Parminder Bhatia, Kathleen McKeown, and Yaser Al-Onaizan. 2020. Severing the Edge Between Before and After: Neural Architectures for Temporal Ordering of Events. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 5412–5417, Online. Association for Computational Linguistics.