Stress Test for BERT and Deep Models: Predicting Words from Italian Poetry
This work provides insights into the limitations of deep learning models like BERT for natural language processing in complex, domain-specific tasks such as poetry analysis, but it is incremental as it applies existing methods to new data without major methodological innovations.
The authors tested BERT on Italian poetry to assess its difficulty in predicting words across lexical, syntactic, and semantic levels, finding that the model is highly sensitive to noncanonical structures, word frequency, and local non-literal meanings.
In this paper we present a set of experiments carried out with BERT on a number of Italian sentences taken from poetry domain. The experiments are organized on the hypothesis of a very high level of difficulty in predictability at the three levels of linguistic complexity that we intend to monitor: lexical, syntactic and semantic level. To test this hypothesis we ran the Italian version of BERT with 80 sentences for a total of 900 tokens mostly extracted from Italian poetry of the first half of last century. Then we alternated canonical and noncanonical versions of the same sentence before processing them with the same DL model. We used then sentences from the newswire domain containing similar syntactic structures. The results show that the DL model is highly sensitive to presence of noncanonical structures. However, DLs are also very sensitive to word frequency and to local non literal meaning compositional effect. This is also apparent by the preference for predicting function vs content words, collocates vs infrequent word phrases. In the paper, we focused our attention on the use of subword units done by BERT for out of vocabulary words.