BERT Rediscovers the Classical NLP Pipeline

Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 4593–4601 Florence, Italy, July 28 - August 2, 2019. c⃝2019 Association for Computational Linguistics 4593 BERT Rediscovers the Classical NLP Pipeline Ian Tenney1 Dipanjan Das1 Ellie Pavlick1,2 1Google Research 2Brown University {iftenney,dipanjand,epavlick}@google.com Abstract Pre-trained text encoders have rapidly ad- vanced the state of the art on many NLP tasks. We focus on one such model, BERT, and aim to quantify where linguistic informa- tion is captured within the network. We find that the model represents the steps of the tra- ditional NLP pipeline in an interpretable and localizable way, and that the regions respon- sible for each step appear in the expected se- quence: POS tagging, parsing, NER, semantic roles, then coreference. Qualitative analysis reveals that the model can and often does ad- just this pipeline dynamically, revising lower- level decisions on the basis of disambiguating information from higher-level representations. 1 Introduction Pre-trained sentence encoders such as ELMo (Pe- ters et al., 2018a) and BERT (Devlin et al., 2019) have rapidly improved the state of the art on many NLP tasks, and seem poised to displace both static word embeddings (Mikolov et al., 2013) and dis- crete pipelines (Manning et al., 2014) as the basis for natural language processing systems. While this has been a boon for performance, it has come at the cost of interpretability, and it remains un- clear whether such models are in fact learning the kind of abstractions that we intuitively believe are important for representing natural language, or are simply modeling complex co-occurrence statis- tics. A wave of recent work has begun to “probe” state-of-the-art models to understand whether they are representing language in a satisfying way. Much of this work is behavior-based, designing controlled test sets and analyzing errors in order to reverse-engineer the types of abstractions the model may or may not be representing (e.g. Con- neau et al., 2018; Marvin and Linzen, 2018; Poliak et al., 2018). Parallel efforts inspect the structure of the network directly, to assess whether there exist localizable regions associated with distinct types of linguistic decisions. Such work has pro- duced evidence that deep language models can en- code a range of syntactic and semantic informa- tion (e.g. Shi et al., 2016; Belinkov, 2018; Ten- ney et al., 2019), and that more complex structures are represented hierarchically in the higher layers of the model (Peters et al., 2018b; Blevins et al., 2018). We build on this latter line of work, focusing on the BERT model (Devlin et al., 2019), and use a suite of probing tasks (Tenney et al., 2019) de- rived from the traditional NLP pipeline to quantify where specific types of linguistic information are encoded. Building on observations (Peters et al., 2018b) that lower layers of a language model en- code more local syntax while higher layers capture more complex semantics, we present two novel contributions. First, we present an analysis that spans the common components of a traditional NLP pipeline. We show …

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