Title: [MELA: Multilingual Evaluation of Linguistic Acceptability](https://arxiv.org/abs/2311.09033)
**Abstract**: In this work, we present the largest benchmark to date on linguistic acceptability: Multilingual Evaluation of Linguistic Acceptability -- MELA, with 46K samples covering 10 languages from a diverse set of language families. We establish LLM baselines on this benchmark, and investigate cross-lingual transfer in acceptability judgements with XLM-R. In pursuit of multilingual interpretability, we conduct probing experiments with fine-tuned XLM-R to explore the process of syntax capability acquisition. Our results show that GPT-4o exhibits a strong multilingual ability, outperforming fine-tuned XLM-R, while open-source multilingual models lag behind by a noticeable gap. Cross-lingual transfer experiments show that transfer in acceptability judgment is non-trivial: 500 Icelandic fine-tuning examples lead to 23 MCC performance in a completely unrelated language -- Chinese. Results of our probing experiments indicate that training on MELA improves the performance of XLM-R on syntax-related tasks.
Homepage: https://github.com/sjtu-compling/MELA
### Citation
```
@inproceedings{zhang2023mela,
author = {Ziyin Zhang and
Yikang Liu and
Weifang Huang and
Junyu Mao and
Rui Wang and
Hai Hu},
title = {{MELA:} Multilingual Evaluation of Linguistic Acceptability},
booktitle = {Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), {ACL} 2024, Bangkok, Thailand},
publisher = {Association for Computational Linguistics},
year = {2024},
url = {https://doi.org/10.48550/arXiv.2311.09033}
}
```
### Groups and Tasks
#### Groups
-`mela`: multilingual evaluation of linguistic acceptability
#### Tasks
-`mela_en`: English
-`mela_zh`: Chinese
-`mela_it`: Italian
-`mela_ru`: Russian
-`mela_de`: Germany
-`mela_fr`: French
-`mela_es`: Spanish
-`mela_ja`: Japanese
-`mela_ar`: Arabic
-`mela_ar`: Icelandic
### Checklist
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- [x] Is the task an existing benchmark in the literature?
- [x] Have you referenced the original paper that introduced the task?
- [x] If yes, does the original paper provide a reference implementation? If so, have you checked against the reference implementation and documented how to run such a test?
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- [ ] Is the "Main" variant of this task clearly denoted?
- [ ] Have you provided a short sentence in a README on what each new variant adds / evaluates?
- [ ] Have you noted which, if any, published evaluation setups are matched by this variant?
