A Query-Parallel Machine Reading Comprehension Framework for Low-resource NER
Yuhao Zhang, Yongliang Wang · 2023
Named entity recognition (NER) is a fundamental task in natural language processing.Recently, NER has been formulated as a machine reading comprehension (MRC) task, in which manually-crafted queries are used to extract entities of different types.However, current MRC-based NER techniques are limited to extracting a single type of entities at a time and are largely geared towards resource-rich settings.This renders them inefficient during the inference phase, while also leaving their potential untapped for utilization in low-resource settings.We suggest a query-parallel MRCbased approach to address these issues, which is capable of extracting multiple entity types concurrently and is applicable to both resourcerich and resource-limited settings.Specifically, we propose a query-parallel encoder which uses a query-segmented attention mechanism to isolate the semantics of queries and model the query-context interaction with a unidirectional flow.This allows for easier generalization to new entity types or transfer to new domains.After obtaining the query and context representations through the encoder, they are fed into a query-conditioned biaffine predictor to extract multiple entities at once.The model is trained with parameter-efficient tuning technique, making it more data-efficient.We conduct extensive experiments and demonstrate that our model performs competitively against strong baseline methods in resource-rich settings, and achieves state-of-the-art results in low-resource settings, including training-fromscratch, in-domain transfer and cross-domain transfer tasks.