Multilingual multimodal research focuses on collecting resources, developing models, and evaluating systems that need to jointly reason over multilingual text and multimodal inputs, including images, videos, texts, and knowledge bases. Multilingual multimodal NLP presents new and unique challenges. First, it is one of the areas that suffer the most from language imbalance issues. Texts in most multimodal datasets are usually only available in high-resource languages. Second, multilingual multimodal research provides opportunities to investigate culture-related phenomena. On top of the language imbalance issue in text-based corpora and models, the data of additional modalities (e.g. images or videos) are mostly collected from North American and Western European sources (and their worldviews). As a result, multimodal models do not capture our world's multicultural diversity and do not generalize to out-of-distribution data from minority cultures. The interplay of the two issues leads to extremely poor performance of multilingual multimodal systems in real-life scenarios. This workshop encourages and promotes research efforts towards more inclusive multimodal technologies and tools to assess them. We invite papers which focus on the topics of interest include (but are not limited to):

Datasets for multilingual multimodal learning
Modeling multilingual multimodal Data
Approaches to improving the inclusion of multilingual multimodal models
Evaluation and analysis for multilingual multimodal learning
Future challenges of multilingual multimodal research
Submission Policy

The paper submission will be done via OpenReview:
https://openreview.net/group?id=aclweb.org/ACL/2022/Workshop/MML

Submitted manuscripts must be 8 pages long for full papers, and 4 pages long for short papers. Both full and short papers can have unlimited pages for references and appendices. We follow ARR submission guidelines. For more information about templates, guidelines, and instructions, see the ARR CFP guidelines. We encourage authors to include a broader impact and ethical concerns statement, following ARR Ethics Policy from the main conference.

All submissions will be double-blind peer-reviewed (with author names and affiliations removed) by the program committee and judged by their relevance to the workshop themes.

Please note that at least one of the authors of each accepted paper must register for the workshop and present the paper.

Non-Archival Option
ACL workshops are traditionally archival. To allow dual submission of work, we are also including a non-archival track. If accepted, these submissions will still participate and present their work in the workshop. A reference to the paper will be hosted on the workshop website (if desired), but will not be included in the official proceedings. Please submit through OpenReview but indicate that this is a cross-submission at the bottom of the submission form. You can also skip this step and inform us of your non-archival preference after the reviews.

Shared Task Submission

We will organize a shared task. Papers describing systems that participate in the shared task are welcome to submit to this workshop. Please see the details in a separate call later.

Organizers:

Emanuele Bugliarello, University of Copenhagen
Kai-Wei Chang, University of California, Los Angeles
Desmond Elliott, University of Copenhagen
Spandana Gella, Amazon Alexa AI
Aishwarya Kamath, New York University
Liunian Harold Li, University of California, Los Angeles
Fangyu Liu, University of Cambridge
Jonas Pfeiffer, Technical University of Darmstadt
Edoardo M. Ponti, MILA Montreal
Krishna Srinivasan, Google Research
Ivan Vulic, University of Cambridge
Yinfei Yang, Google Research
Da Yin, University of California, Los Angeles