@inproceedings {7231,
	title = {Are Transformers a Modern Version of ELIZA? Observations on French Object Verb Agreement},
	year = {2021},
	pages = {4599{\textendash}4610},
	publisher = {Association for Computational Linguistics},
	address = {Online and Punta Cana, Dominican Republic},
	abstract = {<p>Many recent works have demonstrated that unsupervised sentence representations of neural networks encode syntactic information by observing that neural language models are able to predict the agreement between a verb and its subject. We take a critical look at this line of research by showing that it is possible to achieve high accuracy on this agreement task with simple surface heuristics, indicating a possible flaw in our assessment of neural networks\&$\#$39; syntactic ability. Our fine-grained analyses of results on the long-range French object-verb agreement show that contrary to LSTMs, Transformers are able to capture a non-trivial amount of grammatical structure.</p>
},
	doi = {10.18653/v1/2021.emnlp-main.377},
	url = {https://aclanthology.org/2021.emnlp-main.377},
	author = {Li, Bingzhi and Wisniewski, Guillaume and Beno{\^\i}t Crabb{\'e}}
}
