Subword tokenization is a fundamental preprocessing step for all the state-of-the-art language models. It is designed to avoid Out-Of-Vocabulary (OOV) issues by segmenting OOV words into subwords. This shared task asks participants to develop a subword tokenization system and follow this procedure:
- pre-train baby language models with your tokenizer
- fine-tune your BabyLM on the first three subtasks with training and development splits
- submit predictions on test splits
- Subtask 1: Word and Definition
- Subtask 2: Word and Word
- Subtask 3: Word and Morphology
Subtask 4: Machine Translation
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Please open the issues if you have any questions.
The average score of all subtasks will determine the shared task winner. Participant teams may submit as many systems as they want.
Classify if a given word and a given definition match semantically.
Training and development data are UTF-8-encoded tab-separated values files. Each example occupies a single line and consists of the input word, the input definition, and the corresponding output category. The following shows two lines of the training data:
clerking the activity of recording business transactions 1
ammo alternatively placed in genus Martynia 0
Classify whether a given word contains a given morphology.
Training and development data are UTF-8-encoded tab-separated values files. Each example occupies a single line and consists of the input word, the input morphology type, and the corresponding output category. The following shows two lines of the training data:
leaderboard derivation 1
overpressing compound 0
Classify whether two words in input are semantically related to one another.
Training and development data are UTF-8-encoded tab-separated values files. Each example occupies a single line and consists of two input words, as well as the corresponding output category. The following shows two lines of the training data:
photocopy mosaic 1
poorer proxy 0
For pre-training your baby language models and training your tokenizer, you can only use the 100M word dataset from the BabyLM Challenge 2023. For training your tokenizer, you can additionally use the English morphological data from the SIGMORPHON Shared Task 2022 on Morpheme Segmentation. Please note: this morphological data had some noises and we will release a more clean version of this data soon here.
We will provide Python evaluation scripts, reporting the following evaluation measures:
- TBA
Babylm baselines for WaD subtask. The first table shows the tokenizer size and how the test split is divided into three subgroups of umLabeller.
| model | tokenizer size | vocab | morph | alien |
|---|---|---|---|---|
| babylm-roberta-base | 50227 | 1001 (33.3%) | 1050 (35.0%) | 949 (31.6%) |
| lgt-bert-babylm | 16385 | 347 (11.5%) | 1078 (35.9%) | 1560 (52.0%) |
The second table shows the test accuracies across three subgroups with total.
| model | vocab | morph | alien | total |
|---|---|---|---|---|
| babylm-roberta-base | 91.6±0.5 | 61.8±1.2 | 53.2±1.6 | 69.0±0.8 |
| lgt-bert-babylm | 93.1±0.5 | 75.2±0.4 | 72.0±0.9 | 75.8±0.4 |
Please submit your team's results to [email protected], CCing your teammates by TBA (AoE). Each submission should be a .tar.gz or .zip file.
- April 15, 2023: The task website is complete, and accepting registrations to the mailing list
- April 29, 2024: Baseline systems released to participants
May 30June 16, 2024: Test data is available for participantsJune 30July 14, 2024: Final Submissions are dueJuly 5July 17, 2024: Results announced to participants- July 30, 2024: System papers due for review
- August 5, 2024: Reviews back to participants
- August 15, 2024: CR deadline; task paper due from organizers.
- Khuyagbaatar Batsuren (University of Melbourne)
- Gábor Bella (University of Trento)
- Verna Dankers (University of Edinburgh)
- Tsetsuukhei Delgerkhuu (National University of Mongolia)
- Omri Uzan (Ben-Gurion University of the Negev)
- Zdeněk Žabokrtský (Charles University)
- Jungyeul Park (University of British Columbia)
- Yuval Pinter (Ben-Gurion University of the Negev)
- Vilém Zouhar (ETH Zürich)
- Ryan Cotterell (ETH Zürich)
- Ekaterina Vylomova (University of Melbourne)