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7thWorkshop on Indian Language Data: Resources and Evaluation (WILDRE)
Venue: Lingotto Conference Centre - Torino, Italy (Organized under LREC-COLING 2024 (25 May 2024))
*Website*: http://sanskrit.jnu.ac.in/conf/wildre7 *Submit papers on - https://softconf.com/lrec-coling2024/WILDRE-7 https://softconf.com/lrec-coling2024/WILDRE-7*
WILDRE-7, the 7th Workshop on Indian Language Data: Resources and Evaluation is proposed to be organised in Lingotto Conference Centre - Torino, Italy under the LREC-COLING platform. India has a huge linguistic diversity and has seen concerted efforts from the Indian government and industry to develop language resources. European Language Resource Association (ELRA) and its associate organizations have been very active and successful in addressing the challenges and opportunities related to language resource creation and evaluation. It is therefore a big opportunity for resource creators of Indian languages to showcase their work on this platform and also to interact and learn from those involved in similar initiatives all over the world. The broader objectives of the WILDRE will be
To map the status of Indian Language Resources
To investigate challenges related to creating and sharing various levels of language resources
To promote a dialogue between language resource developers and users
To provide an opportunity for researchers from India to collaborate with researchers from other parts of the world
Dates for Short/Long papers and Posters and Demos (tentative)
February 28, 2023: Paper submissions due
March 28, 2024: Paper notification acceptance
SUBMISSIONS
Papers must describe original, completed/ in progress and unpublished work. Each submission will be reviewed by three program committee members.
Accepted papers will be given up to 10 pages (for full papers) 5 pages (for short papers and posters) in the workshop proceedings, and will be presented as oral paper or poster.
Papers should be formatted according to the LREC-COLING style sheet, which is provided on the LREC-COLING 2024 website ( https://lrec-coling-2024.org/authors-kit/). Papers should be submitted in PDF format to the LREC-COLING website ( https://softconf.com/lrec-coling2024/wildre-7/)
We are seeking submissions under the following category
Full papers (10 pages)
Short papers (work in progress: 5 pages)
Posters (innovative ideas/proposals, research proposal of students)
Demo (of working online/standalone systems)
WILDRE-7 will have a special focus on Demos of Indian Language Technology. In the past few years, as more resources have been developed and made available, there has been an increased activity in developing usable technology using these. WILDRE-7 would like to encourage and widen the Demo track to allow the community to showcase their demos and have mutually beneficial interactions with each other as well as resource developers.
WILDRE-7 is seeking full, short papers, posters and demos on the following topics related to Indian Language Resources:
Digital Humanities, heritage computing
Corpora - text, speech, multimodal, methodologies, annotation and tools
Lexicons and Machine-readable dictionaries
Ontologies, Grammars
Language resources for NLP/ IR/Speech tasks, tools and Infrastructure for language resources
Standards or specifications for language resources application
Licensing and copyright issues
Data mining
Text summarization
Both submission and review processes will be handled electronically. The review process will be double-blind. The workshop website will provide the submission guidelines and the link for the electronic submission.
When submitting a paper from the START page, authors will be asked to provide essential information about resources (in a broad sense, i.e. technologies, standards, evaluation kits, etc.) that have been used for the work described in the paper or are a new result of your research. Moreover, ELRA encourages all LREC-COLING authors to share the described LRs (data, tools, services, etc.), to enable their reuse, and replicability of experiments, including evaluation ones, etc.
For further information on this initiative, please refer to https://lrec-coling-2024.org/
Shared Task
Following the success of the five WILDRE workshops, WILDRE-7 will include Code-mixed Less-Resourced Sentiment Analysis (Code-mixed) and Discourse Machine Translation (DiscoMT) Shared Tasks. The organizers of shared tasks will provide datasets and evaluation platforms to evaluate systems developed by the participants. For further information on this initiative, please refer to http://sanskrit.jnu.ac.in/conf/wildre7
Workshop Organisers
Girish Nath Jha, Jawaharlal Nehru University, India
Kalika Bali, Microsoft Research India Lab, Bangalore, India
Sobha L, AU-KBC, Anna University, Chennai, India
Atul Kr. Ojha, University of Galway, Ireland & Panlingua Language Processing LLP, India
Workshop contact:
Atul Kr. Ojha, University of Galway, Ireland & Panlingua Language Processing LLP, India, shashwatup9k@gmail.com
Identify, Describe and Share your LRs
Describing your LRs in the LRE Map is now a normal practice in the submission procedure of LREC (introduced in 2010 and adopted by other conferences). To continue the efforts initiated at LREC 2014 about “Sharing LRs” (data, tools, web services, etc.), authors will have the possibility, when submitting a paper, to upload LRs in a special LREC repository. This effort of sharing LRs, linked to the LRE Map for their description, may become a new “regular” feature for conferences in our field, thus contributing to creating a common repository where everyone can deposit and share data.
As scientific work requires accurate citations of referenced work to allow the community to understand the whole context and also replicate the experiments conducted by other researchers, LREC-COLING 2024 endorses the need to uniquely identify LRs through the use of the International Standard Language Resource Number (ISLRN, www.islrn.org), a Persistent Unique Identifier to be assigned to each Language Resource. The assignment of ISLRNs to LRs cited in LREC-COLING papers will be offered at submission time.