Domain-oriented Language Modeling with Adaptive Hybrid Masking and Optimal Transport Alignment

Abstract

Motivated by the success of pre-trained language models such as BERT in a broad range of natural language processing (NLP) tasks, recent research efforts have been made for adapting these models for different application domains. Along this line, existing domainoriented models have primarily followed the vanilla BERT architecture and have a straightforward use of the domain corpus. However, domain-oriented tasks usually require accurate understanding of domain phrases, and such fine-grained phrase-level knowledge is hard to be captured by existing pre-training scheme. Also, the word co-occurrences guided semantic learning of pre-training models can be largely augmented by entity-level association knowledge. But meanwhile, by doing so there is a risk of introducing noise due to the lack of groundtruth word-level alignment. To address the above issues, we provide a generalized domain-oriented approach, which leverages auxiliary domain knowledge to improve the existing pre-training framework from two aspects. First, to preserve phrase knowledge effectively, we build a domain phrase pool as auxiliary training tool, meanwhile we introduce Adaptive Hybrid Masked Model to incorporate such knowledge. It integrates two learning modes, word learning and phrase learning, and allows them to switch between each other. Second, we introduce Cross Entity Alignment to leverage entity association as weak supervision to augment the semantic learning of pre-trained models. To alleviate the potential noise in this process, we introduce an interpretable Optimal Transport based approach to guide alignment learning. Experiments on four domain-oriented tasks demonstrate the superiority of our framework. CCS CONCEPTS• Computing methodologies → Natural language processing.

BORA: Bayesian Optimization for Resource Allocation

“…Recently, the interest on the Wasserstein distance [36] and the associated Optimal Transport theory [37] have been growing due to their successful application in many domains (e.g., imaging, signal processing and analysis, natural language process/generation, human learning, optimization, etc.) [38,39,40,41,42,43,44]. Very briefly, the Wasserstein distance allows to measure the difference between two probability distributions, α and α , independently on the values and the nature of their supports (they can be both discrete, continuous, or one continuous and one discrete).…”