• [Final CfP] Call for Papers: Information Processing & Management (IP&M)

    From Ptaszynski Michal@21:1/5 to All on Fri Jun 10 05:34:32 2022
    Dear Colleagues,

    ** Apologies for cross-posting **

    This is Michal Ptaszynski from Kitami Institute of Technology, Japan.

    This is the final call for papers for the Information Processing &
    Management (IP&M) (IF: 6.222) journal Special Issue on "Science Behind
    Neural Language Models." This special issue is also a Thematic Track at Information Processing & Management Conference 2022 (IP&MC2022),
    meaning, that at least one author of the accepted manuscript will need
    to attend the IP&MC2022 conference. For more information about
    IP&MC2022, please visit: https://www.elsevier.com/events/conferences/information-processing-and-

    The deadline for manuscript submission is June 15, 2022, but your paper
    will be reviewed immediately after submission and will be published as
    soon as it is accepted.

    We hope you will consider submitting your paper. https://www.elsevier.com/events/conferences/information-processing-
    and-management-conference/author-submission/science-behind-neural- language-models

    Best regards,

    Michal PTASZYNSKI, Ph.D., Associate Professor Department of Computer
    Science Kitami Institute of Technology, 165 Koen-cho, Kitami, 090-8507,
    Japan TEL/FAX: +81-157-26-9327 michal@mail.kitami-it.ac.jp

    ===========================================Information Processing & Management (IP&M) (IF: 6.222) Special Issue on
    "Science Behind Neural Language Models" & Information Processing &
    Management Conference 2022 (IP&MC2022) Thematic Track on "Science Behind
    Neural Language Models"


    The last several years showed explosive popularity of neural language
    models, especially large pre-trained language models based on the
    transformer architecture. The field of Natural Language Processing (NLP)
    and Computational Linguistics (CL) experienced a shift from simple
    language models such as Bag-of-Words, and word representations like
    word2vec, or GloVe, to more contextually-aware language models, such as
    ELMo, or more recently, BERT, or GPT including their improvements and derivatives. The general high performance obtained by BERT-based models
    in various tasks even convinced Google to apply it as a default backbone
    in its search engine query expansion module, thus making BERT-based
    models a mainstream, and a strong baseline in NLP/CL research. The
    popularity of large pretrained language models also allowed a major
    growth of companies providing freely available repositories of such
    models, and, more recently, the founding of Stanford University�fs
    Center for Research on Foundation Models (CRFM). However, despite the overwhelming popularity, and undeniable performance of large pretrained language models, or �gfoundation models�h, the specific inner-workings
    of those models have been notoriously difficult to analyze and the
    causes of - usually unexpected and unreasonable - errors they make,
    difficult to untangle and mitigate. As the neural language models keep
    gaining in popularity while expanding into the area of multimodality by incorporating visual and speech information, it has become the more
    important to thoroughly analyze, fully explain and understand the
    internal mechanisms of neural language models. In other words, the
    science behind neural language models needs to be developed.

    Aims and scope

    With the above background in mind, we propose the following Information Processing & Management Conference 2022 (IP&MC2022) Thematic Track and Information Processing & Management Journal Special Issue on Science
    Behind Neural Language Models. The TT/SI will focus on topics deepening
    the knowledge on how the neural language models work. Therefore, instead
    of taking up basic topics from the fields of CL and NLP, such as
    improvement of part-of-speech tagging, or standard sentiment analysis, regardless of whether they apply neural language models in practice, we
    will focus on promoting research that specifically aims at analyzing and understanding the �gbells and whistles�h of neural language models, for which the generally perceived science has not been established yet.

    Target audience

    The TT/SI will aim at the audience of scientists, researchers, scholars,
    and students performing research on the analysis of pretrained language
    models, with a specific focus on explainable approaches to language
    models, analysis of errors such models make, methods for debiasing, detoxification and other methods of improvement of the pretrained
    language models. The TT/SI will not accept research on basic NLP/CL
    topics for which the field has been well established, such as
    improvement of part-of-speech tagging, sentiment analysis, etc., even if
    they apply neural language models unless they directly contribute to
    furthering the understanding and explanation of the inner workings of
    large scale pretrained language models.

    List of Topics

    List of Topics The Thematic Track / Special Issue will invite papers on
    topics listed, but not limited to the following:
    - Neural language model architectures
    - Improvement of neural language model generation process
    - Methods for fine tuning and optimization of neural language models
    - Debiasing neural language models
    - Detoxification of neural language models
    - Error analysis and probing of neural language models
    - Explainable methods for neural language models
    - Neural language models and linguistic phenomena
    - Lottery Ticket Hypothesis for neural language models
    - Multimodality in neural language models
    - Generative neural language models
    - Inferential neural language models
    - Cross-lingual or multilingual neural language models
    - Compression of neural language models
    - Domain specific neural language models
    - Expansion of information embedded in neural language models

    Important Dates:

    Thematic track manuscript submission due date; authors are welcome to
    submit early as reviews will be rolling: June 15, 2022 Author
    notification: July 31, 2022 IP&MC conference presentation and feedback:
    October 20-23, 2022 Post conference revision due date: January 1, 2023

    Submission Guidelines:

    Submit your manuscript to the Special Issue category (VSI: IPMC2022
    HCICTS) through the online submission system of Information Processing & Management. https://www.editorialmanager.com/ipm/

    Authors will prepare the submission following the Guide for Authors on
    IP&M journal at (https://www.elsevier.com/journals/information-processing-and- management/0306-4573/guide-for-authors). All papers will be peer-
    reviewed following the IP&MC2022 reviewing procedures.

    The authors of accepted papers will be obligated to participate in IP&MC
    2022 and present the paper to the community to receive feedback. The
    accepted papers will be invited for revision after receiving feedback on
    the IP&MC 2022 conference. The submissions will be given premium
    handling at IP&M following its peer-review procedure and, (if accepted), published in IP&M as full journal articles, with also an option for a
    short conference version at IP&MC2022.

    Please see this infographic for the manuscript flow: https://www.elsevi- er.com/__data/assets/pdf_file/0003/1211934/IPMC2022Timeline10Oct2022.pdf

    For more information about IP&MC2022, please visit https://www.elsevier.com/events/conferences/information-processing-and-

    Thematic Track / Special Issue Editors:

    Managing Guest Editor: Michal Ptaszynski (Kitami Institute of

    Guest Editors: Rafal Rzepka (Hokkaido University) Anna Rogers
    (University of Copenhagen) Karol Nowakowski (Tohoku University of
    Community Service and Science)

    For further information, please feel free to contact Michal
    Ptaszynski directly.

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