Bayesian Supervised Domain Adaptation for Short Text Similarity

摘要

Identification of short text similarity ( STS ) is a high-utility NLP task with applications in a variety of domains. We explore adaptation of STS algorithms to different target domains and applications. A two-level hierarchical Bayesian model is employed for domain adaptation ( DA ) of a linear STS model to text from different sources (e.g., news, tweets). This model is then further extended for multitask learning ( MTL ) of three related tasks: STS , short answer scoring ( SAS ) and answer sentence ranking ( ASR ). In our experiments, the adaptive model demonstrates better overall cross-domain and cross-task performance over two non-adaptive baselines.