@inproceedings{kohli-etal-2022-arguably,
title = "{ARGUABLY} @ Causal News Corpus 2022: Contextually Augmented Language Models for Event Causality Identification",
author = "Kohli, Guneet and
Kaur, Prabsimran and
Bedi, Jatin",
editor = {H{\"u}rriyeto{\u{g}}lu, Ali and
Tanev, Hristo and
Zavarella, Vanni and
Y{\"o}r{\"u}k, Erdem},
booktitle = "Proceedings of the 5th Workshop on Challenges and Applications of Automated Extraction of Socio-political Events from Text (CASE)",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates (Hybrid)",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.case-1.20",
doi = "10.18653/v1/2022.case-1.20",
pages = "143--148",
abstract = "Causal (a cause-effect relationship between two arguments) has become integral to various NLP domains such as question answering, summarization, and event prediction. To understand causality in detail, Event Causality Identification with Causal News Corpus (CASE-2022) has organized shared tasks. This paper defines our participation in Subtask 1, which focuses on classifying event causality. We used sentence-level augmentation based on contextualized word embeddings of distillBERT to construct new data. This data was then trained using two approaches. The first technique used the DeBERTa language model, and the second used the RoBERTa language model in combination with cross-attention. We obtained the second-best F1 score (0.8610) in the competition with the Contextually Augmented DeBERTa model.",
}
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<abstract>Causal (a cause-effect relationship between two arguments) has become integral to various NLP domains such as question answering, summarization, and event prediction. To understand causality in detail, Event Causality Identification with Causal News Corpus (CASE-2022) has organized shared tasks. This paper defines our participation in Subtask 1, which focuses on classifying event causality. We used sentence-level augmentation based on contextualized word embeddings of distillBERT to construct new data. This data was then trained using two approaches. The first technique used the DeBERTa language model, and the second used the RoBERTa language model in combination with cross-attention. We obtained the second-best F1 score (0.8610) in the competition with the Contextually Augmented DeBERTa model.</abstract>
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%0 Conference Proceedings
%T ARGUABLY @ Causal News Corpus 2022: Contextually Augmented Language Models for Event Causality Identification
%A Kohli, Guneet
%A Kaur, Prabsimran
%A Bedi, Jatin
%Y Hürriyetoğlu, Ali
%Y Tanev, Hristo
%Y Zavarella, Vanni
%Y Yörük, Erdem
%S Proceedings of the 5th Workshop on Challenges and Applications of Automated Extraction of Socio-political Events from Text (CASE)
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, United Arab Emirates (Hybrid)
%F kohli-etal-2022-arguably
%X Causal (a cause-effect relationship between two arguments) has become integral to various NLP domains such as question answering, summarization, and event prediction. To understand causality in detail, Event Causality Identification with Causal News Corpus (CASE-2022) has organized shared tasks. This paper defines our participation in Subtask 1, which focuses on classifying event causality. We used sentence-level augmentation based on contextualized word embeddings of distillBERT to construct new data. This data was then trained using two approaches. The first technique used the DeBERTa language model, and the second used the RoBERTa language model in combination with cross-attention. We obtained the second-best F1 score (0.8610) in the competition with the Contextually Augmented DeBERTa model.
%R 10.18653/v1/2022.case-1.20
%U https://aclanthology.org/2022.case-1.20
%U https://doi.org/10.18653/v1/2022.case-1.20
%P 143-148
Markdown (Informal)
[ARGUABLY @ Causal News Corpus 2022: Contextually Augmented Language Models for Event Causality Identification](https://aclanthology.org/2022.case-1.20) (Kohli et al., CASE 2022)
ACL