[Sigwac] Fwd: 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*

*================================================*

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/
<https://emw.ku.edu.tr/case-2022/>





*Motivation*

*================================================*

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*

*================================================*

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*

*================================================*

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
<https://github.com/tanfiona/CausalNewsCorpus>*

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





*Important Dates*

*================================================*

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*

*================================================*

   - Fiona Anting Tan, Institute of Data Science/ National University of
   Singapore, Singapore, tan.f at u.nus.edu
   - Ali Hürriyetoğlu, Koc University, Turkey, ahurriyetoglu at ku.edu.tr
   - Tommaso Caselli, Rijksuniversiteit Groningen, Netherlands,
   t.caselli at rug.nl
   - Nelleke Oostdijk, Radboud University, nelleke.oostdijk at ru.nl
   - Tadashi Nomoto, National Institute of Japanese Literature, Japan,
   nomoto at acm.org
   - Onur Uca, Mersin University, onuruca at mersin.edu.tr
   - Iqra Ameer, Centro de Investigación en Computación/ Instituto
   Politécnico Nacional, Mexico, iqra at nlp.cic.ipn.mx
   - Hansi Hettiarachchi, Birmingham City University, United Kingdom,
   hansi.hettiarachchi at mail.bcu.ac.uk
   - Farhana Ferdousi Liza, University of East Anglia, United Kingdom,
   f.liza at uea.ac.uk
   - Tiancheng Hu, ETH Zürich, Switzerland, tianhu at ethz.ch

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



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