ArgFuse: A Weakly-Supervised Framework for Document-Level Event Argument Aggregation

Kar, Debanjana; Sarkar, Sudeshna; Goyal, Pawan

doi:10.18653/v1/2021.case-1.5

Cited by 3 publications

(1 citation statement)

References 17 publications

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“…Given a sentence "The earthquake generates a tsunami that rose up to 135 feet", an ECI model should identify that a causal relationship holds between events "earthquake" and "tsunami". ECI is a crucial task in natural language processing area and can support many intelligent applications, such as future event prediction [1,2], event extraction [3], machine reading comprehension [4], and question answering [5].…”

Section: Introductionmentioning

confidence: 99%

PTEKC: Pre-training with Event knowledge of ConceptNet for Cross-lingual Event Causality Identification

Zhu,

Yu,

Huang

et al. 2024

Preprint

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Event Causality Identification (ECI) task aims to identify causal relations between events in texts, which is beneficial to understanding the precise logic meaning expressed by a text. Although existing event causality identification works based on fine-tuned PLMs have achieved promising results, they suffer from prohibitive computation costs, catastrophic forgetting of distributional knowledge, as well as poor interpretability. Particularly in low-resource and cross-linguistic scenarios, existing multilingual models are generally confronted with the so-called curse of multilinguality, language bias, and hence result in low accuracy and generalization ability. In this paper, we propose a paradigm, termed Pre-training with Event Knowledge of ConceptNet (PTEKC), to couple Multilingual Pre-trained Language Models (MPLMs) with external event knowledge for Cross-lingual Event Causality Identification. Specifically, we have developed a parameter-sharing adapter plugin that facilitates the integration of event knowledge into the frozen Pretrained Language Model (PLM). This approach significantly diminishes the number of trainable parameters and greatly reduces the risk of catastrophic forgetting. Our Adapter integrates multilingual-aligned event knowledge into the MPLMs through two designed pre-training tasks, namely span masking and self-supervised link prediction. Extensive experiments on the benchmark dataset MECI show that PTEKC is parameter-efficient and can effectively incorporate knowledge for improving cross-lingual event causality identification.

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Section: Introductionmentioning

confidence: 99%

PTEKC: Pre-training with Event knowledge of ConceptNet for Cross-lingual Event Causality Identification

Zhu,

Yu,

Huang

et al. 2024

Preprint

View full text Add to dashboard Cite

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Challenges and Applications of Automated Extraction of Socio-political Events from Text (CASE 2021): Workshop and Shared Task Report

Hürriyetoğlu¹,

Tanev²,

Zavarella³

et al. 2021

Proceedings of the 4th Workshop on Challenges and Applications of Automated Extraction of Socio-Political Events From Text (CAS

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This workshop is the fourth issue of a series of workshops on automatic extraction of sociopolitical events from news, organized by the Emerging Market Welfare Project, with the support of the Joint Research Centre of the European Commission and with contributions from many other prominent scholars in this field. The purpose of this series of workshops is to foster research and development of reliable, valid, robust, and practical solutions for automatically detecting descriptions of sociopolitical events, such as protests, riots, wars and armed conflicts, in text streams. This year workshop contributors make use of the stateof-the-art NLP technologies, such as Deep Learning, Word Embeddings and Transformers and cover a wide range of topics from text classification to news bias detection. Around 40 teams have registered and 15 teams contributed to three tasks that are i) multilingual protest news detection, ii) fine-grained classification of socio-political events, and iii) discovering Black Lives Matter protest events. The workshop also highlights two keynote and four invited talks about various aspects of creating event data sets and multi-and cross-lingual machine learning in few-and zero-shot settings.

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Challenges and Applications of Automated Extraction of Socio-political Events from Text (CASE 2022): Workshop and Shared Task Report

Hürriyetoğlu

Tanev²,

Zavarella³

et al. 2022

Proceedings of the 5th Workshop on Challenges and Applications of Automated Extraction of Socio-Political Events From Text (CAS

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We provide a summary of the fifth edition of the CASE workshop that is held in the scope of EMNLP 2022. The workshop consists of regular papers, two keynotes, working papers of shared task participants, and task overview papers. This workshop has been bringing together all aspects of event information collection across technical and social science fields. In addition to the progress in depth, the submission and acceptance of multimodal approaches show the widening of this interdisciplinary research topic.

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ArgFuse: A Weakly-Supervised Framework for Document-Level Event Argument Aggregation

Abstract: Most of the existing information extraction frameworks Veyseh

Cited by 3 publications

References 17 publications

PTEKC: Pre-training with Event knowledge of ConceptNet for Cross-lingual Event Causality Identification

PTEKC: Pre-training with Event knowledge of ConceptNet for Cross-lingual Event Causality Identification

Challenges and Applications of Automated Extraction of Socio-political Events from Text (CASE 2021): Workshop and Shared Task Report

Challenges and Applications of Automated Extraction of Socio-political Events from Text (CASE 2022): Workshop and Shared Task Report

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