LIMIT-BERT : Linguistic Informed Multi-Task BERT
This work addresses the challenge of improving natural language processing models by integrating linguistic knowledge, offering incremental advancements in multi-task learning for syntax and semantics tasks.
The paper tackles the problem of learning language representations across multiple linguistic tasks by introducing LIMIT-BERT, a linguistically informed multi-task BERT model, which achieves new state-of-the-art or competitive results on semantic and syntactic parsing benchmarks such as Propbank and Penn Treebank.
In this paper, we present a Linguistic Informed Multi-Task BERT (LIMIT-BERT) for learning language representations across multiple linguistic tasks by Multi-Task Learning (MTL). LIMIT-BERT includes five key linguistic syntax and semantics tasks: Part-Of-Speech (POS) tags, constituent and dependency syntactic parsing, span and dependency semantic role labeling (SRL). Besides, LIMIT-BERT adopts linguistics mask strategy: Syntactic and Semantic Phrase Masking which mask all of the tokens corresponding to a syntactic/semantic phrase. Different from recent Multi-Task Deep Neural Networks (MT-DNN) (Liu et al., 2019), our LIMIT-BERT is linguistically motivated and learning in a semi-supervised method which provides large amounts of linguistic-task data as same as BERT learning corpus. As a result, LIMIT-BERT not only improves linguistic tasks performance but also benefits from a regularization effect and linguistic information that leads to more general representations to help adapt to new tasks and domains. LIMIT-BERT obtains new state-of-the-art or competitive results on both span and dependency semantic parsing on Propbank benchmarks and both dependency and constituent syntactic parsing on Penn Treebank.