LT4HALA 2026
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EvaLatin
INTRODUCTION
The LT4HALA 2026 workshop will also be the venue of the forth edition of EvaLatin, the campaign totally devoted to the evaluation of NLP tools for Latin. The campaign is designed with the aim of answering two questions:
- How can we promote the development of resources and language technologies for the Latin language?
- How can we foster collaboration among scholars working on Latin and attract researchers from different disciplines?
EvaLatin 2026 edition will have 2 tasks, i.e. Dependency Parsing and Named Entity Recognition.
Shared test data and an evaluation script will be provided to the participants who will choose to participate in either one or all tasks.
IMPORTANT DATES
- 22 December 2025: guidelines available
- Evaluation Window I - Task: Dependency Parsing
- 3 February 2026: test data available
- 10 February 2026: system results due to organizers
- Evaluation Window II - Task: Named Entity Recognition
- 12 February 2026: test data available
- 19 February 2026: system results due to organizers
- 10 March 2026: reports due to organizers
- 20 March 2026: short report review deadline
- 27 March 2026: camera ready version of reports due to organizers
DATA
Dependency parsing
The dependency parsing task is based on the Universal Dependencies framework.
Named Entity Recognition
TBA
EVALUATION
TBA
HOW TO PARTICIPATE
Participants will be required to submit their runs and to provide a technical report that should include a brief description of their approach, focusing on the adopted algorithms, models and resources, a summary of their experiments, and an analysis of the obtained results. Technical reports will be included in the proceedings as short papers: the maximum length is 4 pages (excluding references) and they should follow the LREC 2026 official format). Reports will receive a light review (we will check for the correctness of the format, the exactness of results and ranking, and overall exposition). Reports should be submitted using the START submission page of the workshop (TBA).
Participants are allowed to use any approach (e.g. from traditional machine learning algorithms to Large Language Models) and any resource (annotated and non-annotated data, embeddings): all approaches and resources are expected to be described in the systems’ reports.
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