LC4: First workshop on Low Resource Cross-Domain, Cross-Lingual and Cross-Modal Offensive Content Analysis |
Website | https://lc4workshop.github.io/ |
Submission link | https://easychair.org/conferences/?conf=lc4 |
Submission deadline | September 20, 2022 |
LC4 workshop puts the emphasis more on (a) understanding and studying how offensive contents vary across different domains and genre (b) developing cross-lingual approaches to bridge gap between languages that have large corpora available and low resource languages and (c) how different modality interaction could be used to better quantify offensive contents. The broader goal of this workshop is to address questions such as the following
This workshop does not only attempt to leverage focus on low cross domain and cross lingual offensive content in social media but also motivate their application in industry and society.
- How change in domain of data impacts the overall generated offensive contents and the systems so developed?
- How to efficiently transfer knowledge from one language with abundant supervision information to another language with less or even no data?
- How to align cross-modal data by using appropriate alignment functions and similarity measurements for better quantifying offensive contents?
- How to better utilize different languages' data in an optimal way to identify various types of offensive contents?
Submission Guidelines
Please submit your LC4 workshop paper through easy chair. When preparing your submission, please adhere strictly to the instructions here . These requirements serve to ensure the appropriateness of the reviewing process and inclusion in the Springer proceedings as part of SPELLL 2022.
List of Topics
Authors are invited to submit full papers of up to 12 pages of content and short papers of up to 6 pages of content, with unlimited pages for references. Accepted papers will be given an additional page of content to address reviewer comments. Previously published papers cannot be accepted. Papers that are currently undergoing review at other venues are welcome. Topics related to developing computational models and systems include but are not limited to
- Multimodal models and methods for detecting cross domain offensive contents online, including, but not limited to hate speech, gender-based violence, cyberbullying, homophobia etc.
- Application of NLP and Computer Vision tools to analyze social media content catered to code mixed low resource Dravidian languages
- NLP, Computer Vision and Speech Processing models for low resource cross-lingual offensive content detection.
- Computational models for multi-modal offensive content detection with emphasis on handling absence of one or more modalities
- Development of corpora and annotation guidelines for cross domain, cross modal, multi modal and cross lingual offensive content analysis
- Critical evaluation of systems with a focus on Low Resource Cross-Domain, Cross-Lingual and Cross-Modal Offensive Content Analysis
- Systems studying model and social biases under cross domain low resource settings for offensive content analysis
- Cross-domain metrics, which can reliably and robustly measure the quality of system outputs from multiple modalities (e.g., image and speech), different domains (e.g., movie reviews, homophobic contents) and different languages.
- Study of quality of annotations for cross domain low resource offensive contents, e.g., consistency of annotations, inter-rater agreement, and bias etc.
Committees
Program Committee
- Anushiya Rachel Gladston, Vellore Institute of Technology, Chennai
- Dhanalakshmi V, Subramania Bharathi School of Tamil Language & Literature, Pondicherry University, India
- Dhivya Chinnappa, Thomson Reuters
- Joel Raymann, University of Waterloo, Canada
- Krishna Madgula, Zopsmart Technologies
- Onkar Krishna, Hitachi Ltd, Japan
- Ramesh Kannan R, Vellore Institute of Technology, Chennai
- Saima Mohan, Hitachi India R&D
- Sathyaraj, T, Sri Krishna Adithya College of Arts and Science, Coimbatore
- Sharath Kumar, Hitachi India R&D
- Soubraylu Sivakumar, KL University, Vijayawada
- Yuta Koreeda, Hitachi Ltd, Japan
Organizing committee
- Manikandan Ravikiran, R&D Center, Hitachi India Pvt Ltd
- Satyanarayanan N. Aakur, Oklahoma State University
- Ratnavel Rajalakshmi, Vellore Institute of Technology, Chennai
- Ananth Ganesh, R&D Center, Hitachi India Pvt Ltd
- Yuichi Nonaka, R&D Center, Hitachi India Pvt Ltd
- Shibani Antonette , University of Technology Sydney
- Dileep Aroor Dinesh, Indian Institute Of Technology–Mandi
Publication
LC4 proceedings will be published in SPELLL2022 by Springer
Contact
All questions about submissions should be emailed to lc4workshop2022@gmail.com