Fwd: Call for papers for the conference track in FIRE 2022
Debasis Ganguly
Call for Papers
FIRE 2022: 14th meeting of the Forum for Information Retrieval Evaluation
9th - 13th December 2022
Hybrid Conference, Kolkata, India
Submission Deadline: 9th September 2022
Website : fire.irsi.res.in
The 14th meeting of the Forum for Information Retrieval Evaluation 2022 will be a hybrid conference at Kolkata, India. We are seeking the submission of high-quality and original paper. Submissions will be reviewed by experts on the basis of the originality of the work, the validity of the results, chosen methodology, writing quality and the overall contribution to the field of IR. Authors are encouraged to describe work in progress and late-breaking research results.
Topics of interest include, but are not limited to
Search and Ranking: Research on core algorithmic topics in IR:
Queries and query analysis (e.g., Query understanding, query reformulation, query representation etc.)
Retrieval models and ranking (e.g., Cross lingual IR, ranking algorithms, language models, retrieval algorithms, learning to rank etc)
Efficiency and scalability (e.g., distributed search, search engine architecture, indexing, crawling etc)
Domain Specific: Research on domain specific IR:
Applications in Social Media (e.g., Hate speech recognition, social network in search etc.)
Applications in Finance (e.g., Stock market prediction, other applications in finance)
Applications in Legal (e.g., Patent discovery, verdict summarization, other applications in law)
Applications in Health (e.g., Biomedical IR, medicine, genomics, other applications in health)
Other applications and domains
Recommendation Systems: Research on Recommendation systems, content representation and content analysis:
Recommendation algorithms (e.g., Content based filtering, collaborative filtering etc.)
Document representation and analysis (e.g., Summarization, text representation, sentiment analysis etc.)
Knowledge acquisition (e.g., Information extraction, event extraction etc.)
Multimodal and Crossmodal IR/Recsys model
Visual Question Answering
Image search/recommendation
Multimodal document summarization
Bridging the gap between AI and IR:
Supervised/Weakly supervised deep neural networks
Word Embedding Methodologies
Question answering
Conversational systems
Machine Translation
Evaluation: Research on evaluation of IR systems:
User centric evaluation (e.g., User experience, user engagement etc)
System centric evaluation (e.g., Evaluation metrics)
Explainability, Fairness and Trust of IR/Recsys models
Explainable models for ranking, text classification/clustering, summarization etc.
User studies for explainable AI (XAI) applied to IR/Recsys
Issues related to fairness and trustworthiness of IR/Recsys models
Important dates
| 17th July 2022 | Paper submission link available |
| 9th September 2022 | Paper submission deadline |
| 30th October 2022 | Paper acceptance notification |
| 11th November 2022 | Camera ready copy submission deadline |
| 9th-13th December 2022 | Hybrid Conference |
Note: All submission deadlines are 11:59 PM AoE Time Zone (Anywhere on Earth).
Submission Guidelines
Submissions must describe substantial, original and unpublished work. Wherever appropriate, concrete evaluation and analysis must be included. If the paper being submitted is under review at any other venue, the same should be explicitly mentioned when making the submission. Such a paper, if accepted, should be withdrawn from all other places.
The FIRE conference track, this year, is subdivided into 4 different subtracks (described later), each with a different scope and objective, and with different reviewing policy. Please make sure that you are submitting your paper in the correct track. No requests for switching papers across tracks will be entertained after the deadline for paper submission expires.
Paper Template and Submission
The submitted papers must follow ACM SIG's template available on https://www.acm.org/publications/taps/word-template-workflow . The only accepted format of submissions is PDF. Papers which do not conform to the requirements may get rejected without review. Please note that it is the responsibility of the authors to ensure that the PDF submission has been uploaded successfully (we suggest that you try downloading your paper again yourself, to check). Authors are invited to submit in any of the following tracks:
Regular paper
Similar to last year, this year also we don’t make any explicit distinction between long and short papers. More time will be allocated for longer papers during the presentation. Submitted papers can be of variable length ranging from a minimum of 2 pages to a maximum of 9 pages of content (excluding references).
Reviewing policy: Double-blind.
Reproducibility Track
In addition to papers that describe original contributions, this year we are also encouraging researchers to submit extensions of papers describing work that was conducted as a part of their submission to a shared task either in FIRE itself (current or previous years) or other evaluation forums. In such a case, the paper submitted at this venue must be associated with a cover letter (limited to at most one page) that explicitly mentions the shared task paper (already published or to be published) and clearly explains the difference between the submitted version and the one that already exists. We encourage submissions that are associated with at least 40% new content. Without associated cover letters such papers will be rejected.
In addition to extensions of authors’ own papers, we also invite papers that seek to reproduce results from a number of other different papers (may be of different authors). Such papers don’t need the cover letter.
Reviewing policy: Single-blind.
Number of pages: Variable - min: 4, max 9 pages of content (excluding references).
Resource and Demo paper
The papers submitted at this track should describe data or software resources towards a research problem that will be helpful to the IR/NLP/AI community. Such resources should ideally be made publicly available for reviewers to judge the merit of the resources. The demo papers should contain a link to a working software that demonstrates the application of existing research methods as a proof-of-the-concept.
Reviewing policy: Single-blind.
Number of pages: Max 4 pages of content (excluding references).
Extended Abstract
This track invites submissions of extended abstracts of papers that have been published elsewhere (e.g., at flagship IR/NLP conferences). Ideally, the extended abstract should summarize the existing paper and contain a perspective on new ideas towards further extending that work or present a bigger picture on how that work fits in a much broader context of existing research. The submitted paper must cite the paper on which further perspective is provided.
Reviewing policy: Single-blind.
Number of pages: Max 4 pages of content (excluding references).
Double-blind Reviewing Policy
All submissions to the regular track of FIRE conference 2022 will be reviewed on the basis of originality, relevance, importance and clarity. For papers submitted to the regular track, the authors must not mention their names or institutional details anywhere in the paper. Authors should refer to themselves in third person when citing their own work. Expressions like "In our earlier work..." or "We previously showed that..." must be avoided.
Presentation Requirements
If accepted, at least one author will have to register for the conference and present their work either in-person or online. For the online mode, the authors would need to pre-record their presentation but are also required to be available during the QA session at the end of the presentation.
Overall co-ordinators
Prasenjit Majumder (DA-IICT)
Mandar Mitra (ISI Kolkata)
Conference Track Chairs
Debasis Ganguly (University of Glasgow)
Surupendu Gangopadhyay (DA-IICT)
Paolo Rosso
We kindly invite you to participate at the PAN-2023 task on Profiling
Cryptocurrency Influencers with Few-Shot Learning.
This task is being held as part of CLEF 2023, and all participating
teams will be able to publish their system description paper at the
CLEF working notes proceedings.
This task focuses on the author profiling of cryptocurrency
influencers in social media from a low-resource perspective, that is,
with little training data. Moreover, we propose to profile types of
influencers also using a low-resource setting. Specifically, we focus
on English Twitter posts for three different sub-tasks:
- Low-resource influencer profiling (subtask-1): profile authors
according to their degree of influence (null, nano, micro, macro, mega).
- Low-resource influencer interest profiling (subtask-2): profile
authors according to their main interests or areas of influence
(technical information, price update, trading matters, gaming, other).
- Low-resource influencer intent profiling (subtask-3): profile
authors according to the intent of their messages (subjective opinion,
financial information, advertising, announcement).
Important Links
Task Website: https://pan.webis.de/clef23/pan23-web/author-profiling.html
Slack workspace: https://pan2023profil-0q48349.slack.com/
Important Dates
February 20, 2023: Training data ready
May 10, 2023: Early bird software submission phase (optional)
May 29, 2023: Software submission deadline
June 05, 2023: Participant paper submission
September 18-21, 2023: Conference
Task organizers
Mara Chinea-Rios (Symanto) Contact Email: mara....@symanto.com
Francisco Rangel (Symanto)
Marc Franco-Salvador (Symanto)
Paolo Rosso (Universitat Politècnica de València)
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Paolo Rosso
Universitat Politècnica de València, Spain
http://personales.upv.es/prosso
Paolo Rosso
interested in boosting research to automatically detect machine
generated text? 🤖👩🏻
We cordially invite all researchers and practitioners from all fields
to participate in the AuTexTification task. If interested, register
yourself in the shared task through this link: https://lnkd.in/dzBZsYiD
Once registered and training phase started, the datasets will be sent
to your email along with a password. Look for more information
regarding task description, schedules, or submissions through the
Autextification web page: https://sites.google.com/view/autextification
More information on the shared task
The new era of automatic content generation has surged through
powerful causal language models like GPT, PALM, or Bloom that can be
used to spread untruthful news, human-looking reviews, or opinions.
Thus, it is imperative to develop technology to automatically detect
generated text for content moderation and to attribute generated text
to specific models to protect intellectual property or to distill
responsibilities. In this context, we propose the “Automatic Text
Identification” (AuTexTification) shared task, to boost research and
development of automatic systems to detect automatically generated
text, obtained by state-of-the-art language models, in English and
Spanish.
We propose two subtasks: (i) Human or Generated, where given a
text participants will have to determine whether a text has been
automatically generated or not; and (ii) Model Attribution, where
participants will have to determine what model generated a text. The
generation models used to generate the text are of increasing number
of neural parameters, ranging from 2 to 175 billion, meaning that
participants' systems should be versatile enough to detect a diverse
set of text generation models and writing styles.
In the training phase, participants will be provided with two
partitions for subtask 1, i.e., English and Spanish partitions, with
binary labels 👩🏻 and 🤖. Similarly, a partition per language will be
released for subtask 2. It will include six labels (A, B, C, D, E, and
F), each label representing a text generation model. Later, the
unlabeled test data will be released.
Important Dates
March 22, 2023: Release of training data
April 21, 2023: Release of test data
May 10, 2023: Participant system results submission
May 17, 2023: Results notification
June 3, 2023: Paper submission
June 16, 2023: Paper peer-reviewed
July 4, 2023: Camera-ready paper version
September 26, 2023: Conference
Task organizers
José Ángel González (Symanto) Contact Email: jose.g...@symanto.com
Areg Sarvazyan (Symanto) Contact Email: areg.sa...@symanto.com
Marc Franco-Salvador (Symanto)
Francisco Rangel (Symanto)
Berta Chulvi (Universitat Politècnica de València)
connect with the other participants and organizers:
https://lnkd.in/di_zaMHf
Paolo Rosso
Paolo Rosso
Universitat Politècnica de València, Spain
Special session on AI for Countering Disinformation Warfare at IJCNN-WCCI
Yokohama (Japan)
June 30 - July 5, 2024
Scope and Topics of Interest
Information disorder encompasses various tactics that contaminate
information streams, including the dissemination of fake news, hoaxes,
hyperpartisan content, and rumors. This phenomenon can significantly
sway public opinion on crucial subjects such as the economy, politics,
and health. Notable instances, such as the Anti-vax movement, posing a
threat to public health, and the QAnon conspiracy or Pizzagate events
in Washington, underscore the detrimental societal impacts spurred by
the spread of false information. After the CoVID-19 crisis prompted
the World Health Organization to coin the term "infodemic" to describe
the surge in fake news surrounding the pandemic. These incidents,
along with others, have fueled a growing interest in comprehending and
combating information disorder. This 2nd edition of the Artificial
Intelligence for Countering Disinformation and Information warfare
aims to appeal to a diverse audience of researchers within the
computational intelligence, machine learning, and deep learning
communities, particularly those focused on disinformation detection
and information warfare analysis. The special session addresses the
need for a collaborative effort across various fields, including
natural language processing, image processing, network analysis—bot
detection, coordinate behavior—, multimedia forensics, and semantic
forensics. By fostering a multidisciplinary approach, this special
session strives to unite researchers from different domains and
encourage innovative strategies to tackle the challenges posed by
information disorder.
The contributions received are expected to provide the research
community with a plethora of applications and novel solutions based on
Deep Learning (including recent architectures such as Transformer),
Natural Language Processing techniques, Evolutionary Computation or
Social Network Analysis, among others. Additionally, this session also
seeks solutions facing new challenges such as multilingualism,
explainability, efficiency and advanced human language understanding
models.
The topics of interest include, but are not limited to the following:
Image and Video Forensics: verification of images and videos, source
identification
New state of the art datasets for disinformation detection
Multimodal approach to disinformation detection and information warfare
Network analysis (bot detection and coordinate behaviours)
Machine and Deep Learning models for disinformation detection
XAI and Fusion methods for disinformation detection
Spreading of disinformation in Social Networks
Success case studies
Analysis and detection of propaganda
Fake news, Rumor, Hoaxes detection
Natural Language Processing for disinformation detection and analysis
Automatic detection of coordinated propaganda campaigns such as the
use of social bots, botnets, seminar users, and Internet water armies
Oppositional thinking analysis: Conspiracy theories vs critical thinking
Organisers
David Camacho, Universidad Politécnica de Madrid, Spain
Alejandro Martín, Universidad Politécnica de Madrid, Spain
Paolo Rosso, Universitat Politècnica de València, Spain
Irene Amerini, Sapienza University of Rome, Italy
Important Dates
15 January 2024. Paper Submission Deadline. Strict deadline
15 March 2024. Paper Acceptance Notification
1 May 2024. Final Paper Submission & Early Registration Deadline
More information at:
https://sites.google.com/view/ai4cdiw2ed/home
https://2024.ieeewcci.org/
Paolo Rosso
ultimate causes of significant events as cover plots orchestrated by
secret, powerful, and malicious groups. A challenging aspect of
identifying conspiracy stems from the difficulty of distinguishing
critical thinking from conspiratorial thinking. This distinction is
vital because labeling a message as conspiratorial when it is only
oppositional could drive those who were simply asking questions into
the arms of the conspiracy communities.
At PAN 2024 we aim at analyzing texts that reflect oppositional
thinking and contain either conspiracy or critical narratives:
https://pan.webis.de/clef24/pan24-web/oppositional-thinking-analysis.html
The task will address two new challenges for the research community:
(1) to distinguish the conspiracy narrative from other oppositional
narratives that do not express a conspiracy mentality (i.e., critical
thinking); and (2) to identify in online messages the key elements of
a narrative that fuels the intergroup conflict in oppositional
thinking. To this end we provide two Telegram text datasets, one
English and one Spanish, and we propose two sub-tasks:
1. Distinguishing between critical and conspiracy texts, a binary
classification task aimed at differentiating between critical messages
that question major decisions in the public health domain, but do not
promote a conspiracist mentality; and messages that view the pandemic
or public health decisions as a result of a malevolent conspiracy by
secret, influential groups.
2. Detecting elements of the oppositional narratives, a token-level
classification task aimed at recognizing text spans corresponding to
the key elements of oppositional narratives, where each annotation
corresponds to a narrative element, and is described by its span and
its category (there are six distinct span categories: AGENT,
FACILITATOR, VICTIM, CAMPAIGNER, OBJECTIVE, NEGATIVE_EFFECT).
We propose the task both in English and in Spanish. Although we
recommend to participate in both languages, it is possible to address
the problem just in one language.
The training dataset (where the authors have been anonymised and
neutral labels have been used) can be requested via Zenodo:
https://zenodo.org/records/10680586
IMPORTANT DATES:
February 20, 2024: Train data release
May 30, 2024: Software submission deadline
June 15, 2024: Participant paper submission Midnight CEST [submission]
[paper template]
July 1st, 2024: Peer review notification
July 08, 2024: Camera-ready participant papers submission
September 09-12, 2024: CLEF conference https://clef2024.imag.fr/
Paolo Rosso, co-organiser of the PAN task on Oppositional thinking analysis
Paolo Rosso
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Call for Participation
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First Call for Participation:
EXIST 2024 at CLEF 2024: sEXism Identification in Social neTworks
Website: http://nlp.uned.es/exist2024/
EXIST is a series of scientific events and shared tasks on sEXism
Identification in Social neTworks (EXIST 2021, EXIST 2022, EXIST
2023). EXIST aims to capture sexism in a broad sense, from explicit
misogyny to other subtle expressions that involve implicit sexist
behaviors. The fourth edition of the EXIST shared task will be held as
a Lab in CLEF 2024, on September 9-12, 2024, at the University of
Grenoble Alpes, France.
Since 2021, the primary objective of EXIST campaigns has been the
identification of sexism in tweets. Three corpora of annotated tweets
have been collected for different EXIST tasks. Likewise, the focus of
EXIST 2024 is to detect sexism in text, using the EXIST 2023 dataset,
but we also extend the focus to memes. Memes are images, usually with
text captions, that typically carry humor and spread through social
media, forums, or other digital platforms. They can be used to spread
false information, perpetuate stereotypes or humiliate people.
As in the 2023 edition, this edition will also embrace the Learning
With Disagreement (LeWiDi) paradigm for both the development of the
dataset and the evaluation of the systems. The LeWiDi paradigm does
not rely on a single “correct” label for each example. Instead, the
model is trained to handle and learn from conflicting or diverse
annotations. This enables the system to consider various annotators’
perspectives, biases, or interpretations, resulting in a fairer
learning process.
Participants will be asked to classify tweets and memes (in English
and Spanish) according to the following six tasks:
TASK 1 - Sexism Identification in Tweets: The first subtask is a
binary classification. The systems have to decide whether or not a
given text (tweet) contains sexist expressions or behaviors (i.e., it
is sexist itself, describes a sexist situation or criticizes a sexist
behavior).
TASK 2 - Source Intention in Tweets: For the tweets that have been
classified as sexist, the second task aims to classify each tweet
according to the intention of the person who wrote it. We propose a
ternary classification task: (i) DIRECT sexist message, (ii) REPORTED
sexist message and (iii) JUDGEMENTAL message.
TASK 3 - Sexism Categorization in Tweets: Once a message has been
classified as sexist, the third task aims to categorize the message in
different types of sexism (according to a categorization proposed by
experts and that takes into account the different facets of women that
are undermined). In particular, each sexist tweet must be categorized
in one or more of the following categories: (i) IDEOLOGICAL AND
INEQUALITY, (ii) STEREOTYPING AND DOMINANCE, (iii) OBJECTIFICATION,
(iv) SEXUAL VIOLENCE and (v) MISOGYNY AND NON-SEXUAL VIOLENCE.
TASK 4 - Sexism Identification in Memes: This is a binary
classification task consisting of deciding whether or not a given meme
is sexist.
Task 5: Source Intention in Memes: As in Task 2, this task aims to
categorize the sexists memes according to the intention of the author.
Due to the characteristics of the memes, the REPORTED label is
virtually null, so in this task systems should only classify sexist
memes in DIRECT or JUDGEMENTAL.
Task 6: Sexism Categorization in Memes: This task aims to classify
sexist memes according to the categorization provided for Task 3: (i)
IDEOLOGICAL AND INEQUALITY, (ii) STEREOTYPING AND DOMINANCE, (iii)
OBJECTIFICATION, (iv) SEXUAL VIOLENCE and (v) MISOGYNY AND NON-SEXUAL
VIOLENCE.
Although we recommend to participate in all subtasks, participants are
allowed to participate just in one of them (e.g., subtask 1).
During the training phase, the task organizers will provide to the
participants the manually-annotated EXIST 2024 dataset. For the
evaluation of the teams, the unlabelled test data will be released.
We encourage participation from both academic institutions and
industrial organizations. We invite the participants to register for
the lab at CLEF 2024 Labs Registration site
(https://clef2024-labs-registration.dei.unipd.it/). You will receive
information about how to join the Google Group for the EXIST 2024
shared task.
Important Dates:
* 13 November 2023 - Registration open.
* 4 March 2024 - Training and development sets available.
* 15 April 2024 - Test set available.
* 22 April 2024 - Registration closes.
* 6 May 2024 - Runs submission due to organizers.
* 20 May 2024 - Results notification to participants.
* 3 June 2024 - Submission of Working Notes by participants.
* 19 June 2024 - Notification of acceptance (peer-reviews).
* 3 July 2024 - Camera-ready participant papers due to organizers.
* 9-12 September 2024 - EXIST 2024 at CLEF Conference.
** Note: All deadlines are 11:59PM UTC-12:00 ("anywhere on Earth") **
Organizers:
Laura Plaza, Universidad Nacional de Educación a Distancia (UNED)
Jorge Carrillo-de-Albornoz, Universidad Nacional de Educación a
Distancia (UNED)
Enrique Amigó, Universidad Nacional de Educación a Distancia (UNED)
Julio Gonzalo, Universidad Nacional de Educación a Distancia (UNED)
Roser Morante, Universidad Nacional de Educación a Distancia (UNED)
Víctor Ruiz García, Universidad Nacional de Educación a Distancia (UNED)
Damiano Spina, Royal Melbourne Institute of Technology (RMIT)
Paolo Rosso, Universitat Politècnica de València (UPV)
Berta Chulvi, Universitat Politècnica de València (UPV)
Alba Maeso Olmos, Universitat Politècnica de València (UPV)
Contact:
Contact the organizers by writing to: jcalb...@lsi.uned.es
Website: http://nlp.uned.es/exist2024/
Paolo Rosso
This shared task will take place as part of IberLEF 2024, the 6th
Workshop on Iberian Languages Evaluation Forum at the SEPLN 2024
Conference, which will be held in Valladolid, Spain on the 26th of
September, 2023.
This is the second version of the AuTexTification at IberLEF 2023
shared task (Sarvazyan et al., 2023). We extend our previous task in
three dimensions: more models, more domains and more languages from
the Iberian Peninsula (in a multilingual fashion), aiming to build
more generalizable detectors and attributors. In this task,
participants must develop models that exploit clues about linguistic
form and meaning to identify automatically generated texts from a wide
variety of models, domains, and languages. We plan to include LLMs
like GPT-3.5, GPT-4, LLaMA, Coral, Command, Falcon, MPT, among others.
New domains like essays, or dialogues, and cover the most prominent
languages from the Iberian Peninsula: Spanish, Catalan, Basque,
Galician, Portuguese, and English (in Gibraltar). We propose two
different subtasks:
· Subtask 1 (Human or Generated): Participants will be
provided a text, and they will have to determine whether the text has
been automatically generated or not. We encourage participants to
develop models that generalize to new LLMs, writing styles, and domains.
· Subtask 2 (Model Attribution): Participants will be provided
an automatically generated text, and they will have to determine what
LLM generated it.
The novelty of this edition is to detect in a multilingual (languages
from the Iberian peninsula such as Spanish, English, Catalan, Gallego,
Euskera, and Portuguese), multi-domain (news, reviews, essays,
dialogues, Wikipedia, wikiHow, tweets, emails, etc.), and multi-model
(GPT, LLaMA, Mistral, Cohere, Anthropic, MPT, Falcon, etc.) setup,
whether a text has been automatically generated or not, and, if
generated, identify the model that generated the text. The datasets of
this edition are built using TextMachina, a Python framework that aids
the creation of high-quality, unbiased datasets to build robust models
for MGT-related tasks such as detection, attribution, boundary, and
mix-case detection.
To foster engagement and reward dedication, we will award the best
participant in each subtask with 500€ sponsored by Genaios.
Important Links
· Task Website: https://sites.google.com/view/iberautextification
· GitHub Repository: https://github.com/Genaios/IberAuTexTification
· Google Groups: https://groups.google.com/g/iberautextification
· Registration: https://sites.google.com/view/iberautextification/data
Important Dates
· March 22, 2023: Release of training data
· April 21, 2023: Release of test data
· May 10, 2023: Participant system results submission
· May 17, 2023: Results notification
· June 3, 2023: Paper submission
· June 16, 2023: Paper peer-reviewed
· July 4, 2023: Camera-ready paper version
· José Ángel González (Genaios) Contact Email:
jose.g...@genaios.ai
· Areg Sarvazyan (Genaios) Contact Email: areg.sa...@genaios.ai
· Marc Franco-Salvador (Genaios)
· Francisco Rangel (Genaios)
· Paolo Rosso (Universitat Politècnica de València)
Please reach out to the organizers [1] or join the Slack [2] workspace
to connect with the other participants and organizers.
[1] organizers.au...@gmail.com+
[2]
https://join.slack.com/t/iberautextification/shared_invite/zt-2c28ezgwy-lHHM6ASHnqLY2YQ8mlPgdQ&sa=D&sntz=1&usg=AOvVaw1oYekQiDZ0_C_-N79NtReu