Have Attention Heads in BERT Learned Constituency Grammar?

Abstract

With the success of pre-trained language models in recent years, more and more researchers focus on opening the "black box" of these models. Following this interest, we carry out a qualitative and quantitative analysis of constituency grammar in attention heads of BERT and RoBERTa. We employ the syntactic distance method to extract implicit constituency grammar from the attention weights of each head. Our results show that there exist heads that can induce some grammar types much better than baselines, suggesting that some heads act as a proxy for constituency grammar. We also analyze how attention heads' constituency grammar inducing (CGI) ability changes after fine-tuning with two kinds of tasks, including sentence meaning similarity (SMS) tasks and natural language inference (NLI) tasks. Our results suggest that SMS tasks decrease the average CGI ability of upper layers, while NLI tasks increase it. Lastly, we investigate the connections between CGI ability and natural language understanding ability on QQP and MNLI tasks.

Acceptability Judgements via Examining the Topology of Attention Maps

“…A scope of approaches has been proposed to interpret the roles of hundreds of attention heads in encoding linguistic properties (Htut et al, 2019;Wu et al, 2020) and identify how the most influential ones benefit the downstream performance (Voita et al, 2019;Jo and Myaeng, 2020). Prior work has demonstrated that heads induce grammar formalisms and structural knowledge (Zhou and Zhao, 2019;Luo, 2021), and linguistic features motivate attention patterns (Kovaleva et al, 2019;Clark et al, 2019). Recent studies also show that certain heads can have multiple functional roles (Pande et al, 2021) and even perform syntactic functions for typologically distant languages (Ravishankar et al, 2021).…”