NER-MQMRC: Formulating Named Entity Recognition as Multi Question Machine Reading Comprehension

• URL: http://arxiv.org/abs/2205.05904v1
• Date: Thu, 12 May 2022 06:54:03 GMT
• Title: NER-MQMRC: Formulating Named Entity Recognition as Multi Question Machine Reading Comprehension
• Authors: Anubhav Shrimal, Avi Jain, Kartik Mehta, Promod Yenigalla
• Abstract summary: We propose posing NER as a multi-question MRC task, where multiple questions are considered at the same time for a single text. We 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.
• Score: 6.3066273316843775
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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