Pre-trained text encoders have rapidly advanced the state of the art on many NLP tasks. We focus on one such model, BERT, and aim to quantify where linguistic information is captured within the network. We find that the model represents the steps of the traditional NLP pipeline in an interpretable and localizable way, and that the regions responsible for each step appear in the expected sequence: POS tagging, parsing, NER, semantic roles, then coreference. Qualitative analysis reveals that the model can and often does adjust this pipeline dynamically, revising lowerlevel decisions on the basis of disambiguating information from higher-level representations.
Researchers have also found proof that BERT is able to represent the classical NLP pipeline REF in a localized interpretable manner in the sequence of POS tagging, parsing, NER, semantics, and coreference.
BERT Rediscovers the Classical NLP Pipeline
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (2019) 4593-4601
Computer Science