Task 1: Hope Speech Detection for Equality, Diversity and Inclusion

Hope is considered significant for the well-being, recuperation and restoration of human life by health professionals. Hope speech reflects the belief that one can discover pathways to one's desired objectives and become motivated to utilize those pathways[1-5]. Our work aims to change the prevalent way of thinking by moving away from a preoccupation with discrimination, loneliness or the worst things in life to building confidence, support and good qualities based on comments by individuals. The goal of this task is to identify whether a comment contains hope speech or not. The comment/post may contain more than one sentence but the average sentence length of the corpora is 1. Each comment/post is annotated at a comment/post level. This dataset also has class imbalance problems depicting real-world scenarios.

The participants will be provided development, training and test dataset in English, Tamil, Spanish, Kannada, and Malayalam. To download the data and participate, go to codalab and click “Participate tab”.

To the best of our knowledge, this is the first shared task on Hope Speech Detection.

Codalab link: https://competitions.codalab.org/competitions/36393

Organizers

1. Bharathi Raja Chakravarthi, Insight SFI Research Centre for Data Analytics, Data Science Institute, National University of Ireland Galway

2. Vigneshwaran Muralidaran, Department of Computer Science and Engineering, Amrita Vishwa Vidyapeetham, Chennai, India

3. Ruba Priyadharshini, Madurai Kamaraj University, India

4. Subalalitha Chinnaudayar Navaneethakrishnan, Department Of Computer Science & Engineering, SRM Institute Of Science And Technology, Tamil Nadu

5. John Phillip McCrae, Data Science Institute, National University of Ireland Galway

6. Miguel Ángel García-Cumbreras, Universidad de Jaén, Spain

7. Salud María Jiménez-Zafra, Universidad de Jaén, Spain

8. Rafael Valencia-García, Universidad de Murcia, Spain

Student Volunteer:

• Prasanna Kumar Kumaresan, Indian Institute of Information Technology and Management-Kerala

• Rahul Ponnusamy, Indian Institute of Information Technology and Management-Kerala

• Daniel García-Baena, Universidad de Jaén, Spain

• José Antonio García-Díaz, Universidad de Murcia, Spain

Task 2: Homophobia/Transphobia Detection in social media comments: Participants will be provided with sentences in comment, extracted from social. Given a comments, a system must predict whether or not it contains any form of homophobia/transphobia. The seed data for this task is the Homophobia/Transphobia Detection dataset [7], a collection of comments from social media. The comments are manually annotated to show whether the text contains homophobia/transphobia.

Codalab Link: https://competitions.codalab.org/competitions/36394

Organizers:

1. Bharathi Raja Chakravarthi, Insight SFI Research Centre for Data Analytics, Data Science Institute, National University of Ireland Galway

2. Ruba Priyadharshini, Madurai Kamaraj University, Tamil Nadu, India

3. Durairaj Thenmozhi, SSN College of Engineering, Tamil Nadu, India

4. John Phillip McCrae, Insight SFI Research Centre for Data Analytics, Data Science Institute, National University of Ireland Galway

5. Paul Buitelaar, Insight SFI Research Centre for Data Analytics, Data Science Institute, National University of Ireland Galway

Student Volunteer:

• Rahul Ponnusamy, Indian Institute of Information Technology and Management-Kerala

• Prasanna Kumar Kumaresan, Indian Institute of Information Technology and Management-Kerala

Task 3: Speech Recognition for Vulnerable Individuals in Tamil:

This shared task addresses a challenging area in Automatic Speech Recognition: vulnerable old-aged and transgender people in Tamil. People in their old-age visit primary locations such as banks, hospitals and administrative offices to address their needs in their quotidian lives. Many aged people are unaware of using the equipment facilitated to aid people. Similarly, transgender people are deprived of primary education because of prejudice in society, so speech is the only medium that could assist them in satisfying their needs. The spontaneous speech data is gathered from old-aged and transgender people, who are bereft of using these facilities to their advantage. The speech corpus containing 5.5 hours of transcribed speech will be released for the training set, and 2 hours of speech data will be released for testing.

Codalab Link: https://competitions.codalab.org/competitions/36408

Organizers:

1. Bharathi B, SSN College of Engineering, Tamil Nadu

2. Bharathi Raja Chakravarthi, Insight SFI Research Centre for Data Analytics, Data Science Institute, National University of Ireland Galway

3. Subalalitha Chinnaudayar Navaneethakrishnan, Department Of Computer Science & Engineering, SRM Institute Of Science And Technology, Tamil Nadu

4. Sripriya N, SSN College of Engineering, Tamil Nadu

Student Volunteer:

• Arunaggiri Pandian, Thiagarajar College of Engineering, India.

• Swetha Valli, Thiagarajar College of Engineering, India.

Task 4: Detecting Signs of Depression from Social Media Text:

Depression is a common mental illness that involves sadness and lack of interest in all day-to-day activities [8][9]. Detecting depression is important since it has to be observed and treated at an early stage to avoid severe consequences [10]. DepSign-LT-EDI@ACL-2022 aims to detect the signs of depression of a person from their social media postings wherein people share their feelings and emotions. Given social media postings in English, the system should classify the signs of depression into three labels namely “not depressed”, “moderately depressed”, and “severely depressed”.

Codalab Link: https://competitions.codalab.org/competitions/36410

Organizers:

1. Thenmozhi Durairaj, Sri Sivasubramaniya Nadar College Of Engineering, Chennai.

2. Bharathi Raja Chakravarthi, Insight SFI Research Centre for Data Analytics, Data Science Institute, National University of Ireland Galway

3. Jerin Mahibha C, Meenakshi Sundarrajan College of Engineering, Chennai.

4. Kayalvizhi Sampath, Sri Sivasubramaniya Nadar College of Engineering, Chennai.

Important Dates for shared task:

Task announcement: Nov 20, 2021

Release of Training data: Nov 20, 2021

Release of Test data: Jan 14, 2022

Run submission deadline: Jan 30, 2022

Results declared: Feb 10, 2022

Paper submission: March 10, 2022

Peer review notification: March 26, 2022

Camera-ready paper due: April 5, 2022

Workshop Dates: May 26-28, 2022

Reference:

[1] Harvey Milk. 1997. The hope speech. We are everywhere: A historical source book of gay and lesbian politics,pages 51–53

[2] Edward C. Chang. 1998. Hope, problem-solving ability, and coping in a college student population: Some implications for theory and practice. Journal of Clinical Psychology, 54(7):953–962

[3] Carolyn M. Youssef and Fred Luthans. 2007. Positive organizational behavior in the workplace: The impact of hope, optimism, and resilience. Journal of Management, 33(5):774–80

[4] Rob Cover. 2013. Queer youth resilience: Critiquing the discourse of hope and hopelessness in lgbt suicide representation.M/C Journal, 16(5).

[5]Snyder, C. R., Harris, C., Anderson, J. R., Holleran, S. A., Irving, L. M., Sigmon, S. T., et al.(1991). The will and the ways: Development and validation of an individual-differences measure of hope. Journal of Personality and Social Psychology, 60, 570-585.

[6] https://www.aclweb.org/anthology/2020.peoples-1.5/

[7] Chakravarthi, B.R., Priyadharshini, R., Ponnusamy, R., Kumaresan, P.K., Sampath, K., Thenmozhi, D., Thangasamy, S., Nallathambi, R. and McCrae, J.P., 2021. Dataset for Identification of Homophobia and Transophobia in Multilingual YouTube Comments. arXiv preprint arXiv:2109.00227.

[8] Institute of Health Metrics and Evaluation. Global Health Data Exchange (GHDx). http://ghdx.healthdata.org/gbd-results-tool?params=gbd-api-2019-permalink/d780dffbe8a381b25e1416884959e88b

[9] Evans-Lacko S, Aguilar-Gaxiola S, Al-Hamzawi A, et al. Socio-economic variations in the mental health treatment gap for people with anxiety, mood, and substance use disorders: results from the WHO World Mental Health (WMH) surveys. Psychol Med. 2018;48(9):1560-1571.

[10] Losada, D. E., Crestani, F., & Parapar, J. (2017, September). eRISK 2017: CLEF lab on early risk prediction on the internet: experimental foundations. In the International Conference of the Cross-Language Evaluation Forum for European Languages (pp. 346-360). Springer, Cham.