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CONCEPTUAL ANALYSIS article

This article is part of the research topic.

Evidence-informed practice for creating meaningful individual special education programs for diverse students with learning disabilities

Beyond English centrality: Integrating expansive conceptions of language for literacy programming into IEPs Provisionally Accepted

• 1 University of Colorado Denver, United States

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This paper addresses the English centrality in reading policy, assessment and instructional practices in the U.S. and its implications for the educational programing for emerging bilingual students (EBs) with disabilities. A recent review of the state of practice as it relates to EBs with disabilities reveals concerns that have endured for nearly six decades: biased assessment, disproportionality issues in special education, and teachers' lack of understanding of language acquisition and students' potential. These concerns demonstrate a need for the field to prioritize multilingual lenses for both the identification of and programming for EBs with disabilities. We propose attention to conceptions of language that expand beyond the structuralist standpoint that prevails in the current science of reading reform. We offer guiding principles for IEP development grounded in sociocultural perspectives when designing bilingual instructional practices, which can be applied to the educational programming for EBs with disabilities. Within a sociocultural view of bilingualism and biliteracy, language and literacy are understood by multiplicities in use, practice, form, and function, in which all communicators draw from expansive meaning-making repertoires, whether in listening, speaking, reading, writing, viewing, and multimodally representing. By expanding conceptions of a student's linguistic repertoire, we honor their use of language as one, holistic system in which their named languages plus a multitude of linguistic practices intersect and interact.

Keywords: bilingual special education1, emerging bilingual students with disabilitiess2, biliteracy3, IEPs4, science of reading

Received: 30 Nov 2023; Accepted: 29 Apr 2024.

Copyright: © 2024 Ferrell, Soltero-González and Kamioka. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Dr. Amy L. Ferrell, University of Colorado Denver, Denver, United States

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Arabic Sign Language Alphabet Classification via Transfer Learning

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• Muhammad Al-Barham 13 ,
• Osama Ahmad Alomari 13 &
• Ashraf Elnagar 14  

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 960))

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The integration of artificial intelligence (AI) has addressed the challenges associated with communication with the deaf community, which requires proficiency in various sign languages. This research paper presents the RGB Arabic Alphabet Sign Language (ArASL) dataset, the first publicly available high-quality RGB dataset. The dataset consists of 7,856 meticulously labeled RGB images representing the Arabic sign language alphabets. Its primary objective is to facilitate the development of practical Arabic sign language classification models. The dataset was carefully compiled with the participation of over 200 individuals, considering factors such as lighting conditions, backgrounds, image orientations, sizes, and resolutions. Domain experts ensured the dataset’s reliability through rigorous validation and filtering. Four models were trained using the ArASL dataset, with RESNET18 achieving the highest accuracy of 96.77%. The accessibility of ArASL on Kaggle encourages its use by researchers and practitioners, making it a valuable resource in the field ( https://www.kaggle.com/datasets/muhammadalbrham/rgb-arabic-alphabets-sign-language-dataset ).

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AbdElghfar, H.A., et al.: A model for qur’anic sign language recognition based on deep learning algorithms. J. Sensors 2023 (2023)

Google Scholar  

Abdulhussein, A.A., Raheem, F.A.: Hand gesture recognition of static letters American sign language (ASL) using deep learning. Eng. Technol. J. 38 (6), 926–937 (2020)

Article   Google Scholar  

Al-Barham, M., et al.: RGB Arabic alphabets sign language dataset. arXiv preprint arXiv:2301.11932 (2023)

ALtememe, M.S., El Abbadi, N.K.: Gesture interpreting of alphabet Arabic sign language based on machine learning algorithms. In: 2022 Iraqi International Conference on Communication and Information Technologies (IICCIT), pp. 177–183. IEEE (2022)

Duwairi, R.M., Halloush, Z.A.: Automatic recognition of Arabic alphabets sign language using deep learning. Int. J. Electr. Comput. Eng. (2088-8708) 12 (3) (2022)

Hdioud, B., Tirari, M.E.H.: A deep learning based approach for recognition of Arabic sign language letters. Int. J. Adv. Comput. Sci. Appl. 14 (4) (2023)

Ito, S.i., Ito, M., Fukumi, M.: Japanese sign language classification based on gathered images and neural networks. Int. J. Adv. Intell. Inform. 5 (3) (2019)

Kumar, K.: DEAF-BSL: deep learning framework for British sign language recognition. Trans. Asian Low-Resour. Lang. Inf. Process. 21 (5), 1–14 (2022)

Latif, G., Mohammad, N., Alghazo, J., AlKhalaf, R., AlKhalaf, R.: Arasl: Arabic alphabets sign language dataset. Data Brief 23 , 103777 (2019)

Pansare, J.R., Gawande, S.H., Ingle, M.: Real-time static hand gesture recognition for American sign language (ASL) in complex background (2012)

Saleh, Y., Issa, G.: Arabic sign language recognition through deep neural networks fine-tuning. Int. J. Online Biomed. Eng. (iJOE) 16 , 71 (2020). https://doi.org/10.3991/ijoe.v16i05.13087

Zakariah, M., Alotaibi, Y.A., Koundal, D., Guo, Y., Mamun Elahi, M., et al.: Sign language recognition for Arabic alphabets using transfer learning technique. Comput. Intell. Neurosci. 2022 (2022)

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Acknowledgment

We extend our sincere appreciation to the Student Counseling Department at the University of Jordan for their valuable guidance and expertise, which greatly contributed to acquiring accurate and appropriate images based on their extensive experience. Furthermore, we express our gratitude to Jana M. AlNatour and Raneem F. Abdelraheem for their valuable assistance in the meticulous data collection process. Additionally, we would like to extend our special thanks to the Research Institute for Sciences and Engineering (RISE) at the University of Sharjah for their support and collaboration.

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Muhammad Al-Barham & Osama Ahmad Alomari

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National University of Computer and Emerging Sciences, Islamabad, Pakistan

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Al-Barham, M., Alomari, O.A., Elnagar, A. (2024). Arabic Sign Language Alphabet Classification via Transfer Learning. In: García Márquez, F.P., Jamil, A., Hameed, A.A., Segovia Ramírez, I. (eds) Emerging Trends and Applications in Artificial Intelligence. ICETAI 2023. Lecture Notes in Networks and Systems, vol 960. Springer, Cham. https://doi.org/10.1007/978-3-031-56728-5_20

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• Category: AI

Tiny but mighty: The Phi-3 small language models with big potential

• Sally Beatty

Sometimes the best way to solve a complex problem is to take a page from a children’s book. That’s the lesson Microsoft researchers learned by figuring out how to pack more punch into a much smaller package.

Last year, after spending his workday thinking through potential solutions to machine learning riddles, Microsoft’s Ronen Eldan was reading bedtime stories to his daughter when he thought to himself, “how did she learn this word? How does she know how to connect these words?” 

That led the Microsoft Research machine learning expert to wonder how much an AI model could learn using only words a 4-year-old could understand – and ultimately to an innovative training approach that’s produced a new class of more capable small language models that promises to make AI more accessible to more people.

Large language models (LLMs) have created exciting new opportunities to be more productive and creative using AI.  But their size means they can require significant computing resources to operate. 

While those models will still be the gold standard for solving many types of complex tasks, Microsoft has been developing a series of small language models (SLMs) that offer many of the same capabilities found in LLMs but are smaller in size and are trained on smaller amounts of data.

The company announced today the Phi-3 family of open models , the most capable and cost-effective small language models available. Phi-3 models outperform models of the same size and next size up across a variety of benchmarks that evaluate language, coding and math capabilities, thanks to training innovations developed by Microsoft researchers.

Microsoft is now making the first in that family of more powerful small language models publicly available: Phi-3-mini , measuring 3.8 billion parameters, which performs better than models twice its size, the company said.

Starting today, it will be available in the Microsoft Azure AI Model Catalog and on Hugging Face , a platform for machine learning models, as well as Ollama , a lightweight framework for running models on a local machine. It will also be available as an NVIDIA NIM  microservice with a standard API interface that can be deployed anywhere. 

Microsoft also announced additional models to the Phi-3 family are coming soon to offer more choice across quality and cost. Phi-3-small (7 billion parameters) and Phi-3-medium (14 billion parameters) will be available in the Azure AI Model Catalog and other model gardens shortly. 

Small language models are designed to perform well for simpler tasks, are more accessible and easier to use for organizations with limited resources and they can be more easily fine-tuned to meet specific needs. 

“What we’re going to start to see is not a shift from large to small, but a shift from a singular category of models to a portfolio of models where customers get the ability to make a decision on what is the best model for their scenario,” said Sonali Yadav, principal product manager for Generative AI at Microsoft.

“Some customers may only need small models, some will need big models and many are going to want to combine both in a variety of ways,” said Luis Vargas, vice president of AI at Microsoft.

Choosing the right language model depends on an organization’s specific needs, the complexity of the task and available resources. Small language models are well suited for organizations looking to build applications that can run locally on a device (as opposed to the cloud) and where a task doesn’t require extensive reasoning or a quick response is needed.

Large language models are more suited for applications that need orchestration of complex tasks involving advanced reasoning, data analysis and understanding of context.  

Small language models also offer potential solutions for regulated industries and sectors that encounter situations where they need high quality results but want to keep data on their own premises, said Yadav. 

Vargas and Yadav are particularly excited about the opportunities to place more capable SLMs on smartphones and other mobile devices that operate “at the edge,” not connected to the cloud. (Think of car computers, PCs without Wi-Fi, traffic systems, smart sensors on a factory floor, remote cameras or devices that monitor environmental compliance.) By keeping data within the device, users can “minimize latency and maximize privacy,” said Vargas. 

Latency refers to the delay that can occur when LLMs communicate with the cloud to retrieve information used to generate answers to users prompts. In some instances, high-quality answers are worth waiting for while in other scenarios speed is more important to user satisfaction.

Because SLMs can work offline, more people will be able to put AI to work in ways that haven’t previously been possible, Vargas said. 

For instance, SLMs could also be put to use in rural areas that lack cell service. Consider a farmer inspecting crops who finds signs of disease on a leaf or branch. Using a SLM with visual capability, the farmer could take a picture of the crop at issue and get immediate recommendations on how to treat pests or disease.  

“If you are in a part of the world that doesn’t have a good network,” said Vargas, “you are still going to be able to have AI experiences on your device.”    

The role of high-quality data  

Just as the name implies, compared to LLMs, SLMs are tiny, at least by AI standards. Phi-3-mini has “only” 3.8 billion parameters – a unit of measure that refers to the algorithmic knobs on a model that help determine its output. By contrast, the biggest large language models are many orders of magnitude larger.

The huge advances in generative AI ushered in by large language models were largely thought to be enabled by their sheer size. But the Microsoft team was able to develop small language models that can deliver outsized results in a tiny package. This breakthrough was enabled by a highly selective approach to training data – which is where children’s books come into play.

To date, the standard way to train large language models has been to use massive amounts of data from the internet. This was thought to be the only way to meet this type of model’s huge appetite for content, which it needs to “learn” to understand the nuances of language and generate intelligent answers to user prompts. But Microsoft researchers had a different idea.

“Instead of training on just raw web data, why don’t you look for data which is of extremely high quality?” asked Sebastien Bubeck, Microsoft vice president of generative AI research who has led the company’s efforts to develop more capable small language models. But where to focus?

Inspired by Eldan’s nightly reading ritual with his daughter, Microsoft researchers decided to create a discrete dataset starting with 3,000 words – including a roughly equal number of nouns, verbs and adjectives. Then they asked a large language model to create a children’s story using one noun, one verb and one adjective from the list – a prompt they repeated millions of times over several days, generating millions of tiny children’s stories.

They dubbed the resulting dataset “TinyStories” and used it to train very small language models of around 10 million parameters. To their surprise, when prompted to create its own stories, the small language model trained on TinyStories generated fluent narratives with perfect grammar.

Next, they took their experiment up a grade, so to speak. This time a bigger group of researchers used carefully selected publicly-available data that was filtered based on educational value and content quality to train Phi-1. After collecting publicly available information into an initial dataset, they used a prompting and seeding formula inspired by the one used for TinyStories, but took it one step further and made it more sophisticated, so that it would capture a wider scope of data. To ensure high quality, they repeatedly filtered the resulting content before feeding it back into a LLM for further synthesizing. In this way, over several weeks, they built up a corpus of data large enough to train a more capable SLM.

“A lot of care goes into producing these synthetic data,” Bubeck said, referring to data generated by AI, “looking over it, making sure it makes sense, filtering it out. We don’t take everything that we produce.” They dubbed this dataset “CodeTextbook.” 

The researchers further enhanced the dataset by approaching data selection like a teacher breaking down difficult concepts for a student. “Because it’s reading from textbook-like material, from quality documents that explain things very, very well,” said Bubeck, “you make the task of the language model to read and understand this material much easier.”

Distinguishing between high- and low-quality information isn’t difficult for a human, but sorting through more than a terabyte of data that Microsoft researchers determined they would need to train their SLM would be impossible without help from a LLM. 

“The power of the current generation of large language models is really an enabler that we didn’t have before in terms of synthetic data generation,” said Ece Kamar, a Microsoft vice president who leads the Microsoft Research AI Frontiers Lab, where the new training approach was developed. 

Starting with carefully selected data helps reduce the likelihood of models returning unwanted or inappropriate responses, but it’s not sufficient to guard against all potential safety challenges. As with all generative AI model releases, Microsoft’s product and responsible AI teams used a multi-layered approach to manage and mitigate risks in developing Phi-3 models.

For instance, after initial training they provided additional examples and feedback on how the models should ideally respond, which builds in an additional safety layer and helps the model generate high-quality results. Each model also undergoes assessment, testing and manual red-teaming, in which experts identify and address potential vulnerabilities.

Finally, developers using the Phi-3 model family can also take advantage of a suite of tools available in Azure AI  to help them build safer and more trustworthy applications.  

Choosing the right-size language model for the right task

But even small language models trained on high quality data have limitations. They are not designed for in-depth knowledge retrieval, where large language models excel due to their greater capacity and training using much larger data sets.

LLMs are better than SLMs at complex reasoning over large amounts of information due to their size and processing power. That’s a function that could be relevant for drug discovery, for example, by helping to pore through vast stores of scientific papers, analyze complex patterns and understand interactions between genes, proteins or chemicals. 

“Anything that involves things like planning where you have a task, and the task is complicated enough that you need to figure out how to partition that task into a set of sub tasks, and sometimes sub-sub tasks, and then execute through all of those to come with a final answer … are really going to be in the domain of large models for a while,” said Vargas.

Based on ongoing conversations with customers, Vargas and Yadav expect to see some companies “offloading” some tasks to small models if the task is not too complex. 

For instance, a business could use Phi-3 to summarize the main points of a long document or extract relevant insights and industry trends from market research reports. Another organization might use Phi-3 to generate copy, helping create content for marketing or sales teams such as product descriptions or social media posts. Or, a company might use Phi-3 to power a support chatbot to answer customers’ basic questions about their plan, or service upgrades.    

Internally, Microsoft is already using suites of models, where large language models play the role of router, to direct certain queries that require less computing power to small language models, while tackling other more complex requests itself.

“The claim here is not that SLMs are going to substitute or replace large language models,” said Kamar. Instead, SLMs “are uniquely positioned for computation on the edge, computation on the device, computations where you don’t need to go to the cloud to get things done. That’s why it is important for us to understand the strengths and weaknesses of this model portfolio.”

And size carries important advantages. There’s still a gap between small language models and the level of intelligence that you can get from the big models on the cloud, said Bubeck. “And maybe there will always be a gap because you know – the big models are going to keep making progress.”

Related links:

• Read more: Introducing Phi-3, redefining what’s possible with SLMs
• Learn more: Azure AI
• Read more: Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone

Top image: Sebastien Bubeck, Microsoft vice president of Generative AI research who has led the company’s efforts to develop more capable small language models. (Photo by Dan DeLong for Microsoft)

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The Evolution of Black-White Differences in Occupational Mobility Across Post-Civil War America

This paper studies long-run differences in intergenerational occupational mobility between Black and White Americans. Combining data from linked historical censuses and contemporary large-scale surveys, we provide a comprehensive set of mobility measures based on Markov chains that trace the short-and long-run dynamics of occupational differences. Our findings highlight the unique importance of changes in mobility experienced by the 1940–1950 birth cohort in shaping the current occupational distribution and reducing the racial occupational gap. We further explore the properties of continuing occupational inequalities and argue that these disparities are better understood by a lack of exchange mobility rather than structural mobility. Thus, contemporary occupational disparities cannot be expected to disappear based on the occupational dynamics seen historically.

More Research From These Scholars

A trajectories-based approach to measuring intergenerational mobility, the great gatsby curve, everybody’s talkin’ at me: levels of majority language acquisition by minority language speakers.

WIDA Assistant Director of Standards

• Madison, Wisconsin
• SCHOOL OF EDUCATION/WIS CENTER FOR EDUCATION RESCH-GEN
• Staff-Full Time
• Opening at: Apr 30 2024 at 14:20 CDT
• Closing at: May 22 2024 at 23:55 CDT

Job Summary:

The WIDA Assistant Director of Standards position is located within the Educator Learning, Research, and Practice (ELRP) team and will report to the Director of Professional Learning Curriculum. This position will be responsible for managing the roadmap for WIDA's English Language Development (ELD) and Spanish Language Development (SLD) standards frameworks, including professional learning content and implementation resources to support educators. The Assistant Director of Standards will manage a team of content developers and work cross-functionally to support research-based updates to the standards frameworks and their associated resources. For more detailed job expectations, please visit our website: https://wida.wisc.edu/about/careers .  Working fully remote is an option for this position within the United States (working remotely internationally is not an option). Requests to work remotely would need to be reviewed based on the UW and School of Education (SoE) remote work policies and go through the SoE implementation process. To learn more about these policies, visit https://businessoffice.education.wisc.edu/human-resources/remote-work/ .  WIDA values linguistic and cultural diversity both as an end and a means for success in the field of education. As such, we strongly encourage applications from a diverse pool of candidates. For more information on WIDA's Mission and Values, please visit https://wida.wisc.edu/about/mission-history  

Responsibilities:

• 30% Plans and directs the day-to-day operational activities of one or multiple research programs or units according to established research objectives in alignment with strategic plans and initiatives
• 20% Assists in the development, coordination, and facilitation of trainings and workshops for internal and external audiences to disseminate research program developments and information
• 10% Plans, develops, and implements processes and protocols to support research aims
• 10% Exercises supervisory authority, including hiring, transferring, suspending, promoting, managing conduct and performance, discharging, assigning, rewarding, disciplining, and/or approving hours worked of at least 2.0 full-time equivalent (FTE) employees
• 20% Serves as a unit liaison and subject matter expert among internal and external stakeholder groups, collaborates across disciplines and functional areas, provides program information, and promotes the accomplishments and developments of scholars and research initiatives
• 5% Monitors program budget(s) and approves unit expenditures
• 5% Develops policies, procedures, and institutional agreements on behalf of the program

Institutional Statement on Diversity:

Diversity is a source of strength, creativity, and innovation for UW-Madison. We value the contributions of each person and respect the profound ways their identity, culture, background, experience, status, abilities, and opinion enrich the university community. We commit ourselves to the pursuit of excellence in teaching, research, outreach, and diversity as inextricably linked goals. The University of Wisconsin-Madison fulfills its public mission by creating a welcoming and inclusive community for people from every background - people who as students, faculty, and staff serve Wisconsin and the world. For more information on diversity and inclusion on campus, please visit: Diversity and Inclusion

Required Master's Degree in education or a related field.

Qualifications:

Required * Minimum 3 years of experience in K-12 curriculum development and/or the development and alignment of K-12 standards. * Demonstrated knowledge of the field of second language acquisition, including the use of English language development (ELD) and Spanish language development (SLD) standards, the role of academic language in content learning, and research-based instructional practices. * Experience in the development of organizational systems, ongoing strategic planning, administration, and management of projects, deadlines, and budgets. * Experience with the development of professional learning content for educators across various mediums. * Superior writing, communication, and organizational skills. * Proficiency in using digital tools for coordinating workflow and documenting plans and outcomes. * Experience supervising and coaching a team and contributing to strategic planning. * Multilingual or bilingual/biliterate in Spanish. Preferred * Familiarity with the WIDA English Language Development (ELD) and Spanish Language Development (SLD) standards. * Familiarity with Universal Design for Learning and andragogy/adult learning. * Direct K-12 classroom experience supporting multilingual learners.

Full Time: 100% It is anticipated this position will be remote and requires work be performed at an offsite, non-campus work location.

Appointment Type, Duration:

Ongoing/Renewable

Minimum $90,000 ANNUAL (12 months) Depending on Qualifications This position offers a comprehensive benefits package, including generous paid time off, competitively priced health/dental/vision/life insurance, tax-advantaged savings accounts, and participation in the nationally recognized Wisconsin Retirement System (WRS) pension fund. For a summary of benefits, please see: https://hr.wisc.edu/benefits/    https://www.wisconsin.edu/ohrwd/benefits/download/fasl.pdf . 

Additional Information:

What is WIDA? For nearly 20 years, WIDA has provided a trusted, comprehensive approach to supporting, teaching, and assessing multilingual learners. We are an educational services organization that advances language development and academic achievement for multilingual children in early childhood and grades K-12. In short, we provide educators around the world with high-quality language standards, assessments, professional learning, and research. We are proud to be a part of the Wisconsin Center for Education Research (WCER) within UW-Madison's nationally ranked School of Education. Visit https://wida.wisc.edu/about/careers   and watch our Introduction to WIDA video to see why WIDA is an incredible place to work! The Wisconsin Center for Education Research (WCER), established in 1964, is one of the first, most productive, and largest university-based education research and development centers in the world. WCER's researchers and staff work to make teaching and learning as effective as possible for all ages and all people. WCER's mission is to improve educational outcomes for diverse student populations, impact education practice positively and foster collaborations among academic disciplines and practitioners. To this end, our center helps scholars and practitioners develop, submit, conduct, and share grant-funded education research. WCER's Commitment to Diversity, Equity & Inclusion: Diversity is a source of strength, creativity, and innovation for UW-Madison and The Wisconsin Center for Education Research (WCER). Individual differences and group diversity inspire creative and equitable outcomes. WCER actively affirms values and seeks to increase diversity in our everyday interactions, practices, and policies. For more information and news about our center, please go to https://wcer.wisc.edu/ . 

How to Apply:

Please click on the "Apply Now" button to start the application process. As part of the application process, you will be required to submit: - A cover letter describing how your experience and qualifications meet the requirements of this position. - A current resume or CV. - A list of at least three professional references, including contact information. A successful applicant will be responsible for ensuring eligibility for employment in the United States on or before the effective date of the appointment.

Kelly Krahenbuhl [email protected] 608-263-4776 Relay Access (WTRS): 7-1-1. See RELAY_SERVICE for further information.

Official Title:

Research Program Manager(RE046)

Department(s):

A17-SCHOOL OF EDUCATION/WCER

Employment Class:

Academic Staff-Renewable

Job Number:

The university of wisconsin-madison is an equal opportunity and affirmative action employer..

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Computer Science > Machine Learning

Title: mer 2024: semi-supervised learning, noise robustness, and open-vocabulary multimodal emotion recognition.

Abstract: Multimodal emotion recognition is an important research topic in artificial intelligence. Over the past few decades, researchers have made remarkable progress by increasing dataset size and building more effective architectures. However, due to various reasons (such as complex environments and inaccurate labels), current systems still cannot meet the demands of practical applications. Therefore, we plan to organize a series of challenges around emotion recognition to further promote the development of this field. Last year, we launched MER2023, focusing on three topics: multi-label learning, noise robustness, and semi-supervised learning. This year, we continue to organize MER2024. In addition to expanding the dataset size, we introduce a new track around open-vocabulary emotion recognition. The main consideration for this track is that existing datasets often fix the label space and use majority voting to enhance annotator consistency, but this process may limit the model's ability to describe subtle emotions. In this track, we encourage participants to generate any number of labels in any category, aiming to describe the emotional state as accurately as possible. Our baseline is based on MERTools and the code is available at: this https URL .

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