BabySLM
Behavioral probing of language acquisition models at the lexical and syntactic level
Install / Use
/learn @MarvinLvn/BabySLMREADME
BabySLM: language-acquisition-friendly benchmark of self-supervised spoken language models [paper link]
Welcome to this repository where you'll find all you need to evaluate your language model at:
- the lexical level using a spot-the-word task (available in audio or phonetic form; see Table 1)
- the syntactic level using a grammatical acceptability judgment task (available in audio, phonetic or orthographic form; see Table 2)
Getting started
You'll probably want to start from there:
- How to download the evaluation data? How to evaluate my own model?
- How to download the training sets?
Examples of stimuli
Stimuli examples can be listened to on this web page.
<center>| Word | Pseudo-word | Word | Pseudo-word | |--------|-------------------------------------------------------------|--------|-------------------------------------------------------------| | hello | lello <br> pello <br> sero <br> dello <br> sello <br> | cookie | kootie <br> koonie <br> roodie <br> rootie <br> boonie <br> |
Table 1: Minimal pairs of real and pseudo-words used in the spot-the-word lexical task.
</center> <center>| Phenomenon | Sentence example | |---------------------------|-----------------------------------------------------------------------| | Adjective-noun order | ✓ The good mom. <br> ✗ The mom good. | | Noun-verb order | ✓ The dragon says. <br> ✗ The says dragon. | | Anaphor-gender agreement | ✓ The dad cuts himself. <br> ✗ The dad cuts herself. | | Anaphor-number agreement | ✓The boys told themselves. <br> ✗ The boys told himself. | | Determiner-noun agreement | ✓ Each good sister. <br> ✗ Many good sister. | | Noun-verb agreement | ✓ The prince needs the princess. <br> ✗ The prince need the princess. |
Table 2: Minimal pairs of grammatical (✓) and ungrammatical (✗) sentences used in the syntactic task.
</center>Reproduce the BabySLM benchmark
If you want to go further:
- How to download the pre-trained models used in the paper and evaluate them?
- How to retrain the models used in the paper?
- How to prepare the training sets used in the paper?
- How to recreate the lexical evaluation?
- How to recreate the syntactic evaluation?
How to cite?
@inproceedings{lavechin2023baby,
title={BabySLM: language-acquisition-friendly benchmark of self-supervised spoken language models},
author={Lavechin, Marvin and Sy, Yaya and Titeux, Hadrien and Bland{\'o}n, Mar{\'\i}a Andrea Cruz and R{\"a}s{\"a}nen, Okko and Bredin, Herv{\'e} and Dupoux, Emmanuel and Cristia, Alejandrina},
year={2023},
booktitle = {Interspeech}
}
Additionnally, if you use BabyBERTa, please cite:
@inproceedings{huebner2021babyberta,
title={BabyBERTa: Learning more grammar with small-scale child-directed language},
author={Huebner, Philip A and Sulem, Elior and Cynthia, Fisher and Roth, Dan},
booktitle={Proceedings of the 25th conference on computational natural language learning},
pages={624--646},
year={2021}
}
If you use the Providence corpus, please cite:
@inproceedings{borschinger2013joint,
title={A joint model of word segmentation and phonological variation for English word-final/t/-deletion},
author={B{\"o}rschinger, Benjamin and Johnson, Mark and Demuth, Katherine},
booktitle={Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
pages={1508--1516},
year={2013}
}
If you use the LibriVox corpus, please cite:
@article{kearns2014librivox,
title={Librivox: Free public domain audiobooks},
author={Kearns, Jodi},
journal={Reference Reviews},
volume={28},
number={1},
pages={7--8},
year={2014},
publisher={Emerald Group Publishing Limited}
}
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