|Title||Are Neural Networks Extracting Linguistic Properties or Memorizing Training Data? An Observation with a Multilingual Probe for Predicting Tense|
|Publication Type||Article dans des actes|
|Année de la conférence||2021|
|Authors||Li, Bingzhi, and Guillaume Wisniewski|
|Nom de la conférence||Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume|
|Publisher||Association for Computational Linguistics|
We evaluate the ability of Bert embeddings to represent tense information, taking French and Chinese as a case study. In French, the tense information is expressed by verb morphology and can be captured by simple surface information. On the contrary, tense interpretation in Chinese is driven by abstract, lexical, syntactic and even pragmatic information. We show that while French tenses can easily be predicted from sentence representations, results drop sharply for Chinese, which suggests that Bert is more likely to memorize shallow patterns from the training data rather than uncover abstract properties.