Exploring syntactic information in sentence embeddings through multilingual subject-verb agreement
<|reference_start|>Exploring syntactic information in sentence embeddings through multilingual subject-verb agreement: In this paper, our goal is to investigate to what degree multilingual pretrained language models capture cross-linguistically valid abstract linguistic representations. We take the approach of developing curated synthetic data on a large scale, with specific properties, and using them to study sentence representations built using pretrained language models. We use a new multiple-choice task and datasets, Blackbird Language Matrices (BLMs), to focus on a specific grammatical structural phenomenon -- subject-verb agreement across a variety of sentence structures -- in several languages. Finding a solution to this task requires a system detecting complex linguistic patterns and paradigms in text representations. Using a two-level architecture that solves the problem in two steps -- detect syntactic objects and their properties in individual sentences, and find patterns across an input sequence of sentences -- we show that despite having been trained on multilingual texts in a consistent manner, multilingual pretrained language models have language-specific differences, and syntactic structure is not shared, even across closely related languages.<|reference_end|>
@article{nastase2024exploring,
    title={Exploring syntactic information in sentence embeddings through
  multilingual subject-verb agreement},
    author={Vivi Nastase and Chunyang Jiang and Giuseppe Samo and Paola Merlo},
    journal={arXiv preprint arXiv:2409.06567},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.06567},
    primaryClass={cs.CL}
}