Apologies for cross-posting!
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URL: https://emw.ku.edu.tr/case-2022/
Sep 7, 2022: Submission deadline on Softconf
Jul 15, 2022: Latest ARR submission deadline for ARR
Oct 2, 2022: Latest ARR commitment deadline
Oct 9, 2022: Notification of Acceptance
Oct 16, 2022: Camera-ready papers due
Workshop dates: Dec 7-8, 2021
Location: Hybrid -> Abu Dhabi & Online
Please see below for the important dates of the shared tasks.
There are two options for submissions that are i) Softconf page of the workshop: https:// https://www.softconf.com/m/icspcc2022 softconf.com/emnlp2022/case2022 and ii) ACL Rolling review (ARR): https://aclrollingreview.org/dates.
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Nowadays, the unprecedented quantity of easily accessible data on social, political, and economic processes offers ground-breaking potential in guiding data-driven analysis in social and human sciences and in driving informed policy-making processes. Governments, multilateral organizations, and local and global NGOs present an increasing demand for high-quality information about a wide variety of events ranging from political violence, environmental catastrophes, and conflict, to international economic and health crises (Coleman et al. 2014; Porta and Diani, 2015) to prevent or resolve conflicts, provide relief for those that are afflicted, or improve the lives of and protect citizens in a variety of ways. Black Lives Matter protests (http://protestmap.raceandpolicing.com) and conflicts in Syria ( https://www.cartercenter.org/peace/conflict_resolution/syria-conflict-resolu...) are only two examples where we must understand, analyze, and improve real-life situations using such data. Finally, these efforts respond to “growing public interest in up-to-date information on crowds” as well ( https://sites.google.com/view/crowdcountingconsortium/faqs).
Event extraction has long been a challenge for the natural language processing (NLP) community as it requires sophisticated methods in defining event ontologies, creating language resources, domain specific grammars, developing Machine Learning models and other algorithmic approaches for various event-detection- specific tasks, such entity detection, semantic labeling, event classification and clustering and others (Pustojevsky et al. 2003; Boroş, 2018; Chen et al. 2021). Social and political scientists have been working to create socio-political event (SPE) databases such as ACLED, EMBERS, GDELT, ICEWS, MMAD, PHOENIX, POLDEM, SPEED, TERRIER, and UCDP following similar steps for decades. These projects and the new ones increasingly rely on machine learning (ML), deep learning (DL), and NLP methods to deal better with the vast amount and variety of data in this domain (Hürriyetoğlu et al. 2020). Unfortunately, automated approaches suffer from major issues like bias, limited generalizability, class imbalance, training data limitations, and ethical issues that have the potential to affect the results and their use drastically (Lau and Baldwin 2020; Bhatia et al. 2020; Chang et al. 2019). Moreover, the results of the automated systems for SPE information collection have neither been comparable to each other nor been of sufficient quality (Wang et al. 2016; Schrodt 2020). SPEs are varied and nuanced. Both the political context and the local language used may affect whether and how they are reported.
We invite contributions from researchers in computer science, NLP, ML, DL, AI, socio-political sciences, conflict analysis and forecasting, peace studies, as well as computational social science scholars involved in the collection and utilization of SPE data.
Academic workshops specific to tackling event information in general or for analyzing text in specific domains such as health, law, finance, and biomedical sciences have significantly accelerated progress in these topics and fields, respectively. However, there has not been a comparable effort for handling SPEs. We fill this gap. We invite work on all aspects of automated coding and analysis of SPEs and events in general from mono- or multi-lingual text sources. This includes (but is not limited to) the following topics
1) Extracting events in and beyond a sentence, event coreference resolution,
2) New datasets, training data collection, and annotation for event information,
3) Event-event relations, e.g., subevents, main events, causal relations,
4) Event dataset evaluation in light of reliability and validity metrics,
5) Defining, populating, and facilitating event schemas and ontologies,
6) Automated tools and pipelines for event collection related tasks,
7) Lexical, syntactic, discursive, and pragmatic aspects of event manifestation,
8) Methodologies for development, evaluation, and analysis of event datasets,
9) Applications of event databases, e.g. early warning, conflict prediction, policymaking,
10) Estimating what is missing in event datasets using internal and external information,
11) Detection of new SPE types, e.g. creative protests, cyberactivism, COVID19 related,
12) Release of new event datasets,
13) Bias and fairness of the sources and event datasets,
14) Ethics, misinformation, privacy, and fairness concerns pertaining to event datasets, and
15) Copyright issues on event dataset creation, dissemination, and sharing.
16) We encourage submissions of new system description papers on our available benchmarks (ProtestNews @ CLEF 2019, AESPEN @ LREC 2020, and CASE @ 2021). Please contact the organizers if you would like to access the data.
The proceedings of the previous editions should be indicative of what we cover: ProtestNews @ CLEF 2019 (http://ceur-ws.org/Vol-2380/), AESPEN @ ACL 2020 (https://aclanthology.org/volumes/2020.aespen-1/), CASE @ ACL-IJCNLP 2021 (https://aclanthology.org/volumes/2021.case-1/).
**** Shared tasks ****
Task 1- Multilingual protest news detection: This is the same shared task organized at CASE 2021 (For more info: https://aclanthology.org/2021.case-1.11/) But this time there will be additional data and languages at the evaluation stage. Contact person: Ali Hürriyetoğlu (ali.hurriyetoglu@gmail.com). Github: https://github.com/emerging-welfare/case-2022-multilingual-event
Task 2- Automatically replicating manually created event datasets: The participants of Task 1 will be invited to run the systems they will develop to tackle Task 1 on a news archive (For more info https://aclanthology.org/2021.case-1.27/). Contact person: Hristo Tanev ( htanev@gmail.com). Github: https://github.com/emerging-welfare/case-2022-multilingual-event
Task 3- Event causality identification: 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 3,559 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 two subtasks (Sequence Classification and Span Detection) 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. Contact person: Fiona Anting Tan (tan.f@u.nus.edu). Github: https://github.com/tanfiona/CausalNewsCorpus
**** Deadlines for the Shared tasks ****
** Task 1 & 2:
Training data available: The training data from CASE 2021 is used.
New test data available: Sept 15, 2022
Test end: Sep 25, 2022
System Description Paper submissions due: Oct 2, 2022
Notification to authors after review: Oct 09, 2022
Camera-ready: Oct 16, 2022
** Task 3:
Training data available: Apr 15, 2022
Validation data available: Apr 15, 2022
Validation labels available: Aug 01, 2022
Test data available: Aug 01, 2022
Test start: Aug 01, 2022
Test end: extended from Aug 15 to Aug 31, 2022
System Description Paper submissions due: Sep 07, 2022
Notification to authors after review: Oct 09, 2022
Camera ready: Oct 16, 2022
*** Keynotes ***
Three prominent scholars have accepted our invitation as keynote speakers:
i) J. Craig Jenkins (https://sociology.osu.edu/people/jenkins.12) is Academy Professor Emeritus of Sociology at The Ohio State University. He directed the Mershon Center for International Security Studies from 2011 to 2015 and is now senior research scientist.
ii) Scott Althaus (https://pol.illinois.edu/directory/profile/salthaus) is Merriam Professor of Political Science, Professor of Communication, and Director of the Cline Center for Advanced Social Research at the University of Illinois Urbana-Champaign.
iii) Thien Huu Nguyen (https://ix.cs.uoregon.edu/~thien/) is an assistant professor in the Department of Computer and Information Science at the University of Oregon. Thien is the director of the NSF IUCRC Center for Big Learning (CBL) at the University of Oregon.
**** Submissions *****
This call solicits short and long papers reporting original and unpublished research on the topics listed above. The papers should emphasize obtained results rather than intended work and should indicate clearly the state of completion of the reported results. The page limits and content structure announced at ACL ARR page (https://aclrollingreview.org/cfp) should be followed for both short and long papers.
Papers should be submitted on the START page of the workshop ( http://softconf.com/emnlp2022/case2022) or on ARR page (TBA on the workshop website) in PDF format, in compliance with the ACL publication author guidelines for ACL publications https://acl-org.github.io/ACLPUB/formatting.html
The reviewing process will be double-blind and papers should not include the author's names and affiliations. Each submission will be reviewed by at least three members of the program committee. The workshop proceedings will be published on ACL Anthology.