*Call for Participation: The Workshop on Holocaust Testimonies as Language
Resources*
Date: 21 May 2024 (full day)
Venue: Lingotto Conference Centre, Turin, Italy
Webpage and programme: https://www.clarin.eu/HTRes2024
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*Registration*
Registration is obligatory and can be carried out via the LREC-COLING 2024
website: https://lrec-coling-2024.org/.
Workshop description
Holocaust testimonies serve as a bridge between survivors and history’s
darkest chapters, providing a connection to the profound experiences of the
past. Testimonies stand as the primary source of information that describe
the Holocaust, offering first-hand accounts and personal narratives of
those who experienced it. The majority of testimonies are captured in an
oral format, as survivors vividly explain and share their personal
experiences and observations from that time period. Transforming Holocaust
testimonies into a machine-processable digital format can be a difficult
task owing to the unstructured nature of the text. The creation of
accessible, comprehensive, and well-annotated Holocaust testimony
collections is of paramount importance to our society. These collections
empower researchers and historians to validate the accuracy of socially and
historically significant information, enabling them to share critical
insights and trends derived from these data. This workshop will investigate
a number of ways in which techniques and tools from natural language
processing and corpus linguistics can contribute to the exploration,
analysis, dissemination and preservation of Holocaust testimonies.
*Programme*
Please refer to the website for the details of the programme:
https://www.clarin.eu/HTRes2024
Contact Email: holocausttlr(a)gmail.com
*Invited speakers*
Silvia Calamai (Siena University)
Michal Frankl (Masaryk Institute and Archives of the Czech Academy of
Sciences)
*Organising Committee*
Isuri Anuradha, University of Wolverhampton, UK
Ingo Frommholz, University of Wolverhampton
Francesca Frontini, CNR-ILC, Italy & CLARIN
Martin Wynne, Oxford University, UK
Ruslan Mitkov, Lancaster University, UK
Paul Rayson, Lancaster University, UK
Alistair Plum, University of Luxembourg, Luxembourg
*Summary*
• Subject: Language & vision-based mobility assistance for visually
impaired people
• Keywords: Assistive Technologies, Visual Impairment, Computer Vision,
Image Captioning
• Research Unit: Lab-STICC (UMR CNRS 6285)
• Team: RAMBO - Robot interaction, Ambient system, Machine learning,
Behaviour, Optimization
• Location: IMT Atlantique, Brest
• Start: September/October 2024
• Duration: 3 years
• Supervision: Panagiotis Papadakis, Christophe Lohr
*Full subject description:*
https://www.imt-atlantique.fr/sites/default/files/recherche/Offres%20de%20t…
*Application *
The candidate must hold (or is about to obtain) a Master Degree in
Computer Science with theoretical and practical skills in AI algorithms
and associated deep-learning tools (e.g. Pytorch), and a solid
background in Computer Vision.
The candidate should be fluent in English (working and publishing main
language), but French speaking is an advantage (meetings with end-users
representatives).
A detailed application should be addressed to
thesis-application-rambo(a)imt-atlantique.fr, including a cover letter, an
up-to-date CV, transcripts of grades (last two years), and a list of
referees.
*Deadline: 17 May 2024
*
SECOND CALL FOR PAPERS
*The Second Workshop on Multimodal Semantic Representations (MMSR II)*
Co-located with ECAI 2024 (https://www.ecai2024.eu/)
19-24 October, Santiago de Compostela, Spain
(workshop on 19 or 20 October)
*Workshop website*: https://mmsr-workshop.github.io/
*Description*
The demand for more sophisticated natural human-computer and human-robot
interactions is rapidly increasing as users become more accustomed to
conversation-like interactions with AI and NLP systems. Such interactions
require not only the robust recognition and generation of expressions
through multiple modalities (language, gesture, vision, action, etc.), but
also the encoding of situated meaning.
When communications become multimodal, each modality in operation provides
an orthogonal angle through which to probe the computational model of the
other modalities, including the behaviors and communicative capabilities
afforded by each. Multimodal interactions thus require a unified framework
and control language through which systems interpret inputs and behaviors
and generate informative outputs. This is vital for intelligent and often
embodied systems to understand the situation and context that they inhabit,
whether in the real world or in a mixed-reality environment shared with
humans.
Furthermore, multimodal large language models appear to offer the
possibility for more dynamic and contextually rich interactions across
various modalities, including facial expressions, gestures, actions, and
language. We invite discussion on how representations and pipelines can
potentially integrate such state-of-the-art language models.
We solicit papers on multimodal semantic representation, including but not
limited to the following topics:
- Semantic frameworks for individual linguistic co-modalities (e.g.
gaze, facial expression);
- Formal representation of situated conversation and embodiment,
including knowledge graphs, designed to represent epistemic state;
- Design, annotation, and corpora of multimodal interaction and meaning
representation;
- Challenges (including cross-lingual and cross-cultural) in multimodal
representation and/or processing;
- Criteria or frameworks for evaluation of multimodal semantics;
- Challenges in aligning co-modalities in formal representation and/or
NLP tasks;
- Design and implementation of neurosymbolic or fusion models for
multimodal processing (with a representational component);
- Methods for probing knowledge of multimodal (language and vision)
models;
- Virtual and situated agents that embody multimodal representations of
common ground.
*Submission Information*
Two types of submissions are solicited: long papers and short papers. Long
papers should describe original research and must not exceed 8 pages,
excluding references. Short papers (typically system or project
descriptions, or ongoing research) must not exceed 4 pages, excluding
references. Both types will be published in the workshop proceedings.
Accepted papers get an extra page in the camera-ready version.
We strongly encourage students to submit to the workshop.
*Important Dates*
May 15, 2024: Submissions due
July 1, 2024: Notification of acceptance decisions
August 2, 2024: Camera-ready papers due
Papers should be formatted using the ECAI style files, available at:
https://www.ecai2024.eu/calls/main-track
Papers will be submitted in PDF format via Chairing Tool at the following
link: https://chairingtool.com/conferences/2MMSR24/MainTrack
Please do not hesitate to reach out with any questions.
Best regards,
Richard Brutti, Lucia Donatelli, Nikhil Krishnaswamy, Kenneth Lai, & James
Pustejovsky
MMSR II organizers
Web page: https://mmsr-workshop.github.io/
Event Notification Type: Test set released.
Website: https://sites.google.com/view/iberautextification
*TEST SET RELEASED*
*IberAuTexTification*
*Automated Text Identification on Languages of the Iberian Peninsula*
Dear All,
The test dataset for the IberAuTexTification 2024 shared task has been
released. It can be found on the shared task website (
https://sites.google.com/view/iberautextification/data), in Genaios Github
repository (https://github.com/Genaios/IberAuTexTification) and Zenodo (
https://zenodo.org/records/11034382).
Please, remember that registration is on a per-team basis, meaning only one
member from each team needs to sign up. Once requested the test dataset on
one of the previous links, you will receive the download permission and a
password to decompress the data within 24 hours. Please, make sure to write
your email address correctly, since we will send passwords, as well as
future notifications to that address.
Please reach out to the organizers or join the Slack workspace to connect
with the other participants and organizers.
Best regards on behalf of
IberAuTexTification shared tasks organizers
SECOND CALL FOR PAPERS
---------------------
The First Annual Meeting of the Special Interest Group on Turkic
Languages (SIGTURK)
August 15-16 2024, Bangkok (Co-located with ACL)
INTRODUCTION
We present the first edition of the SIGTURK Workshop representing the
annual meeting of the ACL Special Interest Group on Turkic Languages,
which aims to provide a new venue that will promote studies on
computational linguistics in Turkic languages. Our main objective is to
bring together an interdisciplinary community of researchers working on
different aspects of natural language processing (NLP) models and
corpora in Turkic languages, providing the recently growing number of
researchers working on the topic with a means of communication and an
opportunity to present their work and exchange ideas.
TOPICS OF INTEREST
We welcome submissions on, but not limited to, the following topics:
* Computational linguistics: models of all aspects of linguistics in
Turkic languages (e.g., semantics, syntax, lexicon, morphology)
* Systems: Case studies on the construction of NLP systems for Turkic
languages
* Evaluation: Understanding the applicability of current NLP methods
in Turkic languages
* Metrics: New metrics and measures for evaluating NLP systems
suitable to Turkic languages
* Learning from sparse data: Novel methods for learning from small or
sparse data in Turkic languages
* Resources: Datasets, benchmarks, and software libraries for NLP
models in Turkic languages
IMPORTANT DATES
* First call for papers: February 9, 2024 (Friday)
* Second call for papers: March 4, 2024 (Monday)
* Paper submission deadline: May 31, 2024 (Friday)
* Notification of acceptance: June 16, 2024 (Monday)
* Camera-ready submission deadline: July 1, 2024 (Monday)
* Workshop dates: August 15-16 (Thursday - Friday)
Note: All deadlines are 11:59 pm UTC -12h (“anywhere on Earth”).
CONTACT INFORMATION
* Email: workshop(a)sigturk.com
* Submission Portal:
https://urldefense.proofpoint.com/v2/url?u=https-3A__openreview.net_group-3…
* Official Website:
https://urldefense.proofpoint.com/v2/url?u=https-3A__sigturk.com_workshop&d…
SUBMISSION GUIDELINES
Research papers
We invite all potential participants to submit their novel research
contributions in the related fields as long papers following the ACL
2024 long paper format (anonymized with 8 pages excluding the
references, and an additional page for the camera-ready versions for the
accepted papers). All accepted research papers will be published as part
of our workshop proceedings and will be presented either through oral
presentations or poster sessions. Our research paper track will accept
submissions through our own submission system available at
https://urldefense.proofpoint.com/v2/url?u=https-3A__openreview.net_group-3…
.
Extended abstracts
Besides long paper submissions, we also invite previously published or
ongoing and incomplete research contributions to our non-archival
extended abstract track. All extended abstracts can use the same EMNLP
template with a 2-page limit, excluding the bibliography. Extended
abstracts can be submitted to the workshop submission system using the
link:
https://urldefense.proofpoint.com/v2/url?u=https-3A__openreview.net_group-3…
.
"HACK TOGETHER" PROGRAMMING EVENT
In addition to the workshop itself, the second day will be devoted to a
full-day collaborative hybrid programming event "Hack Together". Our
goal is to
demonstrate the SIGTURK NLP library and interested parties can
contribute to the integration of new NLP methods and models into the
SIGTURK pipeline. The SIGTURK infrastructure can be found at
https://urldefense.proofpoint.com/v2/url?u=https-3A__github.com_sigturk&d=D…
. Findings of the event will be combined into
a system demonstration paper.
INVITED SPEAKERS
Kemal Oflazer, Carnegie Mellon University
MORE INFORMATION
For further details and updates, please visit our workshop website:
https://urldefense.proofpoint.com/v2/url?u=https-3A__sigturk.com_workshop&d…
ORGANIZERS
Duygu Ataman, New York University
Deniz Zeyrek Bozşahin, Middle East Technical University
Mehmet Oguz Derin
Sardana Ivanova, University of Helsinki
Abdullatif Köksal, LMU Munich
Jonne Sälevä, Brandeis University
The College of Humanities & Social Sciences (CHSS) at Hamad Bin Khalifa
University (HBKU) in Qatar invites applications for the following positions:
1. Full Professor in Digital Humanities with expertise in AI ethics,
bias, and fairness.
2. Associate Professor in Digital Humanities with expertise in Digital
Cultural Heritage, or Digital Media and Communication with a particular
emphasis on the analysis of mis/disinformation (2 positions).
Successful applicants will work closely with our established Digital
Humanities and Societies Program, as well as other programs in the college,
and will develop academic and research collaborations with potential
national, regional, and international partners. They will teach graduate
courses at the MA level, contribute to curriculum development in their
area(s) of specialty, supervise MA and Ph.D. students, and maintain an
active research agenda. The ideal candidates will be dynamic, experienced,
and internationally recognized scholars with an innovative research agenda,
as demonstrated through a strong record of peer-reviewed publications and
funding/grant acquisitions.
Candidates for both positions should hold a Ph.D. or terminal degree from
an accredited university. For the Full Professor position, applicants
should have 10-12 years of relevant full-time university teaching
experience in research, higher education, or closely related areas in
industry (additional experience may be required to show equivalency), with
evidence of increasing professional maturity and productivity. For the
Associate Professor position, applicants should have 6-8 years of relevant
full-time university teaching experience, with evidence of increasing
professional maturity and productivity.
All candidates should demonstrate proficiency in teaching, student
supervision, curriculum development, and have a strong portfolio of
scholarly endeavors. They should also show evidence of participation in
scholarly and academic affairs, an established regional and international
reputation in their discipline, and the ability to work with diverse
groups, cultures, and communities.
To apply, please submit your CV, cover letter, teaching philosophy,
research statement, and the names of three references via the provided link
at the end of this post. Applications will be accepted on a rolling basis
until the positions are filled, with the first review stage starting in May.
Full Professor position: https://www.hbku.edu.qa/en/CHSS-P
Associate professor positions: https://www.hbku.edu.qa/en/CHSS-APR
Hello,
Bloomberg is happy to announce an exciting funding opportunity for Ph.D. students. The seventh edition of the Bloomberg Data Science Ph.D. Fellowship Program invites Ph.D. students working in broadly-construed data science to apply for fellowships.
Our fellowship program, launched in 2018, provides the opportunity for outstanding Ph.D. candidates to be funded for up to three years of their Ph.D. studies to work on their research proposal. The recipients will collaborate and be supported by our Data Science community throughout this time and will complete 14-week summer internships with Bloomberg for the duration of their fellowships. Previous recipients of the fellowship are presented here: 2022-2023, 2021-2022, 2020-2021, 2019-2020, 2018-2019.
Applications for the 2024-2025 academic year must be submitted by May 30, 2024. Fellowship recipients will be announced by July 15, 2024.
Full details about the fellowship, specific topics of interest for this year and application process can be found at: https://www.bloomberg.com/company/values/tech-at-bloomberg/data-science/aca…
We would appreciate it if you can share this opportunity with interested parties.
Please direct all questions and future communications to rdml(a)bloomberg.net.
Bloomberg
Dear all,
We have a PhD opportunity in NLP and computational linguistics about automatic analysis of human ability to collaborate in dyadic and group conversations, for educational applications: [ https://jobs.inria.fr/public/classic/en/offres/2024-07248 | https://jobs.inria.fr/public/classic/en/offres/2024-07248 ] . Though the offer description in the link is in French, we strongly encourage non-French speakers to apply as well! The offer is translated in English below.
Prospective candidates are encouraged to get in touch with us as soon as possible.
Looking forward to reading you,
Maria Boritchev and Chloé Clavel
______________________________________
Automatic analysis of human capacity to collaborate during dyadic and group conversations, for educational applications.
Context and scientific objectives
Work on dialog using NLP and deep learning approaches for Dialog Act prediction or sentiment analysis integrates the conversational aspects by capturing contextual dependencies between utterances using recurrent neural networks (RNN) or convolutional neural networks (CNN) for supervised learning (Bapna et al., 2017). The inter-speaker dynamics has also recently started to be integrated. For example, in (Hazarika et al., 2018), intra-speaker dynamics is modeled using a GRU (Gated Recurrent Unit). Other ways to model a conversation in structures that are more complex than flat sequences of utterances are also investigated by leveraging hierarchical neural architectures (Chapuis et al., 2020) or by using graphs in the neural architectures (Ghosal et al., 2019). The conversational aspects and contextual dependencies between the labels are also modeled using sequential decoders and attention mechanisms for NLP-oriented Dialog Act classification (Colombo et al., 2020). Regarding neural architectures dedicated to generating an agent’s behavior, a few studies on affective computing attempt to integrate collaborative processes. The studies concern the generation of agent’s non-verbal behaviors related to social stances (Dermouche & Pelachaud, 2016) and Long-Short-Term-Memory (LSTM) architectures are used as a black box in order to model inter-speaker dynamics. Other studies that are not relying on neural architectures address the question of selecting the agent’s utterance or best dialog policy (ex. conversation strategies such as hedging or self-disclosure or extroverted or introverted linguistic styles) according to the user’s social behaviors (multimodal behavior in (Ritschel et al., 2017) and verbal behavior in (Pecune & Marsella, 2020)). In both studies, a social reward is built for reinforcement learning. A recent work investigates neural architectures (Bert model named CoBERT) trained on Empathetic conversations for response selection, but there is no option in order to select the level or the kind of empathy which is the most relevant (Zhong et al. 2020).
While these existing neural architectures (convolutional, recurrent and transformer), for tracking a speaker’s state in conversations are extremely promising by modelling inter-speaker dynamics and the sequential structure of the conversation, the phenomena they are detecting are restricted to sentiment, emotions, or dialogue acts. What is still missing in the module dedicated to tracking the user’s state in modular conversational systems is the consideration of the collaborative processes as a joint action of the user and the agent to understand each other, maintain the flow of the interaction and create a social relationship. The aforementioned neural approaches are very effective, but they are not very data-efficient. There are many use cases where the amount of available data is not sufficient to be able to use these methods, particularly when it comes to deep learning; this is notably the case in educational contexts, where the data at stake is quite confidential, especially when children are involved, as the data is considered to be personal data and is therefore subject to GDPR (https://gdpr-info.eu/). Computational linguistics provide us with other approaches to the analysis of conversations, symbolic and logic-based. These approaches rely on small amounts of data and focus on specific phenomena, such as management of implicit implications/information in dialogues (Breitholtz, 2020) and various contexts (Rebuschi, 2017). Segmented Discourse Representation Theory (SDRT, Asher and Lascarides, 2003) is one of the most widely used frameworks for dialogue analysis used within both formal and neural approaches to dialogue. Another approach is to propose a hybridation of knowledge graphs for modelling social commonsense and large language models (Kim et al., 2023).
The objective of the thesis is to investigate approaches that hybridize neural and symbolic models. The approaches will be dedicated to analysing and controlling the level of collaborations between participants in conversations (e.g., misunderstanding analysis and management) through their verbal expressions. We will focus on educational applications such as classroom dynamics & student engagement analysis and conversational systems for supporting students with difficulties, or learning social skills following the ethical guidelines defined in (1).
(1) [ https://web-archive.oecd.org/2020-07-23/559610-trustworthy-artificial-intel… | https://web-archive.oecd.org/2020-07-23/559610-trustworthy-artificial-intel… ]
(Breitholtz, 2020) Breitholtz, E. (2020). Enthymemes in Dialogue. Brill.
(Asher and Lascarides, 2003) Asher, N. and Lascarides, A. (2003). Logics of conversation. Cambridge University Press.
(Rebuschi, 2017) Rebuschi, M. (2017). Schizophrenic conversations and context shifting. In International and Interdisciplinary Conference on Modeling and Using Context, pages 708–721. Springer
(Kim et al., 2023) Hyunwoo Kim, Jack Hessel, Liwei Jiang, Peter West, Ximing Lu, Youngjae Yu, Pei Zhou, Ronan Bras, Malihe Alikhani, Gunhee Kim, Maarten Sap, and Yejin Choi. 2023. SODA: Million-scale Dialogue Distillation with Social Commonsense Contextualization. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 12930–12949, Singapore. Association for Computational Linguistics.
Supervision :
Thesis supervisor: Chloé Clavel, senior research, ALMAnaCH team, Inria Paris
Co-supervisor: Maria Boritchev, associate professor, S2a team, Telecom-Paris