Call for Shared Task Participation: Event Causality Identification with Causal News Corpus at CASE @ RANLP 2023

[ali hürriyetoglu]

Competition Website: 

https://codalab.lisn.upsaclay.fr/competitions/11784

 

 

FIRST CALL FOR PARTICIPATION

CASE-2023 Shared Task: Event Causality Identification with Causal News Corpus

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We invite you to participate in the CASE-2023 Shared Task: Event Causality Identification with Causal News Corpus. 

The task is being held as part of the 6th Workshop on Challenges and Applications of Automated Extraction of Socio-political Events from Text (CASE 2023). All participating teams will be able to publish their system description paper in the workshop proceedings published by ACL.

Workshop Website: https://emw.ku.edu.tr/case-2023/

 

 

Motivation

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Causality is a core cognitive concept and appears in many natural language processing (NLP) works that aim to tackle inference and understanding. We are interested to study event causality in news, and therefore, introduce the Causal News Corpus. 

The Causal News Corpus consists of 3,767 event sentences, extracted from protest event news, that have been annotated with sequence labels on whether it contains causal relations or not. Subsequently, causal sentences are also annotated with Cause, Effect and Signal spans. Our subtasks work on the Causal News Corpus, and we hope that accurate, automated solutions may be proposed for the detection and extraction of causal events in news. 

 

 

 

Task Overview

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We focused on two subtasks relevant to Event Causality Identification:

• Subtask 1: Causal Event Classification – Does an event sentence contain any cause-effect meaning?
• Subtask 2: Cause-Effect-Signal Span Detection – Which consecutive spans correspond to cause, effect or signal per causal sentence?

• Subtask 2.1: Cause-Effect Span Detection – This subtask identifies the spans corresponding to cause and effect per sentence.
• Subtask 2.2: Signal Span Detection – This subtask identifies the spans corresponding to the signal, or causal connective, per cause and effect relation.

Participants may design solutions that work on a single, multiple or all subtasks concurrently. Participants are also allowed to combine Subtask 1 and 2 annotations for either task. However, the target labels of development and test sets should not be introduced during training in their set up in any way (E.g. even for data augmentation).

This is the second iteration of this shared task. The leaderboard from last year is available at https://codalab.lisn.upsaclay.fr/competitions/2299. There are changes for both Subtask 1 and 2 data:

• Added more data. Also revised annotations from previous launch
• Changed traditional P, R, F1 calculations to use FairEval calculations instead

 

Data Content

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Our work extends a prior socio-political news corpus to annotate if event-containing sentences have causal relations or not. Our data sizes and splits are described as follows:

• Subtask 1: Causal Event Classification -- 869 news documents and 3,767 English sentences were annotated with labels on whether it contains causal relations or not. The current data splits are: 3,075 training, 340 development, 352 test.
• Subtask 2: Cause-Effect-Signal Span Detection – Positive causal sentences from Subtask 1 were retained and annotated with Cause-Effect-Signal spans. We annotated 1,982 sentences with 2,754 causal relations. There can be multiple relations per sentence. The data splits for causal relations are: 2,257 training, 249 development, 248 test.

Task Repository: https://github.com/tanfiona/CausalNewsCorpus

Codalab Site: https://codalab.lisn.upsaclay.fr/competitions/11784

 

 

Important Dates

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Training & Validation data available: May 01, 2023

Test data available: Jun 15, 2023

Test start: Jun 15, 2023

Test end: Jun 30, 2023

System Description Paper submissions due: Jul 10, 2023

Notification to authors after review: Aug 05, 2023

Camera ready: Aug 25, 2023

Workshop period @ RANLP: Sep 7-8, 2023

 

 

Organization

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• Fiona Anting Tan, Institute of Data Science/ National University of Singapore, Singapore, ta...@u.nus.edu
• Ali Hürriyetoğlu, Koc University, Turkey, ahurri...@ku.edu.tr
• Tommaso Caselli, Rijksuniversiteit Groningen, Netherlands, t.ca...@rug.nl
• Nelleke Oostdijk, Radboud University, nelleke....@ru.nl 
• Tadashi Nomoto, National Institute of Japanese Literature, Japan, nom...@acm.org
• Onur Uca, Mersin University, onu...@mersin.edu.tr
• Iqra Ameer, Centro de Investigación en Computación/ Instituto Politécnico Nacional, Mexico, iq...@nlp.cic.ipn.mx
• Hansi Hettiarachchi, Birmingham City University, United Kingdom, hansi.het...@mail.bcu.ac.uk
• Farhana Ferdousi Liza, University of East Anglia, United Kingdom, f.l...@uea.ac.uk
• Tiancheng Hu, ETH Zürich, Switzerland, tia...@ethz.ch

Please contact the organizer at ta...@u.nus.edu with your title starting with “CNC ST”, or post questions at the Forum page in Codalab.

 

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