TRACK GOALS AND DETAILS: Two main goals and tasks:
•    Task 1: Propose and develop effective Automatic Metrics for evaluation of open-domain multilingual dialogs.
•    Task 2: Propose and develop Robust Metrics for dialogue systems trained with back translated and paraphrased dialogs in English.
EXPECTED PROPERTIES OF THE PROPOSED METRICS:
•    High correlation with human annotated assessments.
•    Explainable metrics in terms of the quality of the model-generated responses.
•    Participants can propose their own metric or optionally improve the baseline evaluation metric deep AM-FM (Zhang et al, 2020).

TASK 1: METRICS FOR MULTILINGUAL DATA
In this task, the goal for participants is to propose a single metric model effective for the automatic evaluation of multilingual dialogs in English, Spanish and Chinese. The model will provide scores to obtain high correlations with human-annotations.
Participants are expected to use pre-trained or fine-tune multilingual models and train them to predict multidimensional quality metrics by using self-supervised techniques.
TASK 2: ROBUST METRICS
In this task, the goal for participants is to propose robust metrics for automatic evaluation when dealing with English sentences that have been back translated or automatically paraphrased. Here, robustness is understood when using sentences having the same semantic meaning as the original sentence but different wording.
The proposed metric model will be evaluated when comparing the scores produced on the original sentences w.r.t. with the scores produced when using the back-translated/paraphrased sentences. Therefore, the expected performance must be on par with the correlations with human-annotations obtained over the original sentences.

DATASETS:
For training: Up to 18 Human-Human curated multilingual datasets (+3M turns), with turn/dialogue level automatic annotations including QE metrics or toxicity.
Dev/Test: Up to 10 Human-Chatbot curated multilingual datasets (+150k turns), with turn/dialogue level human annotations.

REGISTRATION AND FURTHER INFORMATION:
ChatEval: https://chateval.org/dstc11
GitHub: https://github.com/Mario-RC/dstc11_track4_robust_multilingual_metrics

PROPOSED SCHEDULE:
Training/Validation data release: From November to December in 2022
Test data release: Middle of March in 2023
Entry submission deadline: Middle of March in 2023
Submission of final results: End of March in 2023
Final result announcement: Early of April in 2023
Paper submission: From March to May in 2023
Workshop: July-September/2023 in a venue to be announced with DSTC11

ORGANIZATIONS:
Universidad Politécnica de Madrid (Spain)
National University of Singapore (Singapore)
Tencent AI Lab (China)
New York University (USA)
Carnegie Mellon University (USA)

Mario Rodríguez Cantelar
Postgraduate Non-Doctoral Researcher / PhD student
Centre for Automation and Robotics (UPM-CSIC)