2nd CFP: Special Issue on The Role of Context in Neural Machine Translation Systems and its Evaluation
*Apologies for cross-posting*
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Second CFP: Special Issue on The Role of Context in Neural Machine Translation Systems and its Evaluation in Natural Language Engineering
Guest editors: - Sheila Castilho (The ADAPT Centre, School of Applied Languages and Intercultural Studies, Dublin City University) - Rebecca Knowles (National Research Council Canada)
For this special issue, we invite the submission of papers focusing on the variety of novel implementations of context into neural machine translation systems as well as novel approaches to its evaluation. Recent claims that machine translation systems are reaching (near) human parity at the sentence level have been followed by subsequent analyses that indicate remaining gaps in translation quality at the document level. How best to evaluate machine translation at the document level (and what exactly constitutes document level evaluation) remains an open question. At the same time, there is work seeking to add discourse and context into neural machine translation systems. Papers that focus on topics of context in neural machine translation, machine translation evaluation, or both are welcome.
For full details, see: https://sites.google.com/dcu.ie/nlecontextnmt/home
Topics of interest include, but are not limited to: - Novel language processing techniques for implementing discourse in NMT systems - Document-level NMT and evaluation - Use of target and source context - Context-aware techniques for quality evaluation - Context-aware automatic and human evaluation metrics - The size and composition of the training data and its effect on context-aware systems - The effect of the quality of training data and test sets on context-aware systems - Translationese and its effect on document-level training - Lexical diversity and lexical density in discourse NMT - Discourse NMT for different domains
Publication Timeline: - Article deadline submission: 1 February 2023 - Return of reviews to contributors: 1 April 2023 - Revised articles deadline submission: 1 May 2023 - Return of second reviews to contributors (if applicable): 1 July 2023 - Final Submission: 15 September 2023 - Publication: November 2023 / January 2024
Format and Submission: Typical submissions will be 12-25 pages in length. Authors should follow the "Author Instructions" section on the journal website: https://www.cambridge.org/core/journals/natural-language-engineering/informa...
We highly recommend using the LaTeX template found under "Preparing your materials" at the link above.
All manuscripts must be submitted online via the NLE ScholarOne website: http://mc.manuscriptcentral.com/nle. Under "Special Issue Designation", choose "The Role of Context in Neural Machine Translation Systems and its Evaluation".
Queries: Any queries related to this special issue should be addressed to sheila.castilho@dcu.iemailto:sheila.castilho@dcu.ie with NLE-ContextNMT in the subject line.
(apologies for cross posting)
The Information Fusion Journal (Impact Factor 17.564) organises a Special Issue on "Multimodal Fusion Technologies to counter Disinformation". This special issue is devoted to the revision of current and promising new methods, algorithms and technologies that can be used to help tackling the problem of disinformation.
The CFP can be found at: https://www.sciencedirect.com/journal/information-fusion/about/call-for-pape...
The submission deadline is June 30th, 2023.
The Topics of Interest include:
Dis- and Misinformation / Opinion / Knowledge spread and modelling Methods and techniques for the generation and identification of fabricated and manipulated content (e.g. deep fakes, fake news, fake audios, fake videos) Data knowledge extraction (scraping) for disinformation Technologies for extremism, polarization, and radicalization detection and prevention (e.g. political, religious) AI-supported fact checking and detection of disinformation campaigns Cybercrime: crime detection and investigation methods and techniques for disinformation Real – world case studies and tools against disinformation and misinformation Multimodal fusion methods for disinformation detection and analysis Multimodal approaches for profiling disinformation spreaders Detection and prevention of multimodal content spreading disinformation Detection of fake content and disinformation in Online Social Networks Multimedia methods for disinformation detection and analysis NLP methods for disinformation detection and analysis Machine Learning (e.g. Deep Learning) methods for disinformation detection and analysis Multimodal detection of conspiracy theories
Guest editors:
David Camacho, Universidad Politécnica de Madrid, Madrid, Spain Ioannis Kompatsiaris, Information Technologies Institute, CERTH, Thessaloniki, Greece Paolo Rosso, Universitat Politècnica de València, Valencia, Spain
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Knowles, Rebecca -
Paolo Rosso