NER has been traditionally formulated as a sequence labeling task. However,
there has been recent trend in posing NER as a machine reading comprehension
task (Wang et al., 2020; Mengge et al., 2020), where entity name (or other
information) is considered as a question, text as the context and entity value
in text as answer snippet. These works consider MRC based on a single question
(entity) at a time. We propose posing NER as a multi-question MRC task, where
multiple questions (one question per entity) are considered at the same time
for a single text. We propose a novel BERT-based multi-question MRC (NER-MQMRC)
architecture for this formulation. NER-MQMRC architecture considers all
entities as input to BERT for learning token embeddings with self-attention and
leverages BERT-based entity representation for further improving these token
embeddings for NER task. Evaluation on three NER datasets show that our
proposed architecture leads to average 2.5 times faster training and 2.3 times
faster inference as compared to NER-SQMRC framework based models by considering
all entities together in a single pass. Further, we show that our model
performance does not degrade compared to single-question based MRC (NER-SQMRC)
(Devlin et al., 2019) leading to F1 gain of +0.41%, +0.32% and +0.27% for
AE-Pub, Ecommerce5PT and Twitter datasets respectively. We propose this
architecture primarily to solve large scale e-commerce attribute (or entity)
extraction from unstructured text of a magnitude of 50k+ attributes to be
extracted on a scalable production environment with high performance and
optimised training and inference runtimes.

本研究提出了一种新颖的基于 BERT 的多问题机器阅读理解体系结构，将命名实体识别作为一个多问题 MRC 任务，旨在解决大规模电子商务属性（或实体）在高性能和优化训练和推理运行时从非结构化文本中提取的问题，该模型在三个 NER 数据集上获得了比 NER-SQMRC 框架模型更快的训练和推理时间，并且保持与单个问题 MRC（NER-SQMRC）性能相当。