How much better is performance of SEPT compared to previous state-of-the-art?
SEPT have improvement for Recall  3.9%  and F1 1.3%  over the best performing baseline (SCIIE(SciBERT))
We introduce a new scientific named entity recognizer called SEPT, which stands for Span Extractor with Pre-trained Transformers. In recent papers, span extractors have been demonstrated to be a powerful model compared with sequence labeling models. However, we discover that with the development of pre-trained language models, the performance of span extractors appears to become similar to sequence labeling models. To keep the advantages of span representation, we modified the model by under-sampling to balance the positive and negative samples and reduce the search space. Furthermore, we simplify the origin network architecture to combine the span extractor with BERT. Experiments demonstrate that even simplified architecture achieves the same performance and SEPT achieves a new state of the art result in scientific named entity recognition even without relation information involved.
With the increasing number of scientific publications in the past decades, improving the performance of automatically information extraction in the papers has been a task of concern. Scientific named entity recognition is the key task of information extraction because the overall performance depends on the result of entity extraction in both pipeline and joint models BIBREF0. Named entity recognition has been regarded as a sequence labeling task in most papers BIBREF1. Unlike the sequence labeling model, the span-based model treats an entity as a whole span representation while the sequence labeling model predicts labels in each time step independently. Recent papers BIBREF2, BIBREF3 have shown the advantages of span-based models. Firstly, it can model overlapping and nested named entities. Besides, by extracting the span representation, it can be shared to train in a multitask framework. In this way, span-based models always outperform the traditional sequence labeling models. For all the advantages of the span-based model, there is one more factor that affects performance. The original span extractor needs to score all spans in a text, which is usually a $O(n^2)$ time complexity. However, the ground truths are only a few spans, which means the input samples are extremely imbalanced. Due to the scarcity of annotated corpus of scientific papers, the pre-trained language model is an important role in the task. Recent progress such as ELMo BIBREF4, GPT BIBREF5, BERT BIBREF6 improves the performance of many NLP tasks significantly including named entity recognition. In the scientific domain, SciBERT BIBREF7 leverages a large corpus of scientific text, providing a new resource of the scientific language model. After combining the pre-trained language model with span extractors, we discover that the performance between span-based models and sequence labeling models become similar. In this paper, we propose an approach to improve span-based scientific named entity recognition. Unlike previous papers, we focus on named entity recognition rather than multitask framework because the multitask framework is natural to help. We work on single-tasking and if we can improve the performance on a single task, the benefits on many tasks are natural. To balance the positive and negative samples and reduce the search space, we remove the pruner and modify the model by under-sampling. Furthermore, because there is a multi-head self-attention mechanism in transformers and they can capture interactions between tokens, we don't need more attention or LSTM network in span extractors. So we simplify the origin network architecture and extract span representation by a simple pooling layer. We call the final scientific named entity recognizer SEPT. Experiments demonstrate that even simplified architecture achieves the same performance and SEPT achieves a new state of the art result compared to existing transformer-based systems.