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This paper presents a practical approach to fine-grained information extraction. Through plenty of authors' experiences in practically applying information extraction to business process automation, there can be found a couple of fundamental technical challenges: (i) the availability of labeled data is usually limited and (ii) highly detailed classification is required. The main idea of our proposal is to leverage the concept of transfer learning, which is to reuse the pre-trained model of deep neural networks, with a combination of common statistical classifiers to determine the class of each extracted term. To do that, we first exploit BERT to deal with the limitation of training data in real scenarios, then stack BERT with Convolutional Neural Networks to learn hidden representation for classification. To validate our approach, we applied our model to an actual case of document processing, which is a process of competitive bids for government projects in Japan. We used 100 documents for training and testing and confirmed that the model enables to extract fine-grained named entities with a detailed level of information preciseness specialized in the targeted business process, such as a department name of application receivers.

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Information extraction is a well-known topic that plays a critical role in many NLP applications as its outputs can be considered as an entrance step for digital transformation. However, there still exist gaps when applying research results to actual business cases. This paper introduces AURORA, an information extraction for domainspecific business documents. The intuition of AURORA is to use transfer learning for extraction. To do that, it utilizes the power of transformers for dealing with the limitation of training data in business cases and stacks additional layers for domain adaptation. We demonstrate AURORA in the context of actual scenarios where users are invited to experience two functions: fine-grained and whole paragraph extraction of Japanese business documents. A video of the system is available at http://y2u.be/xHQpYE41Tqw.

Sentence compression is the task of creating a shorter version of an input sentence while keeping important information. In this paper, we extend the task of compression by deletion with the use of contextual embeddings. Different from prior work usually using non-contextual embeddings (Glove or Word2Vec), we exploit contextual embeddings that enable our model capturing the context of inputs. More precisely, we utilize contextual embeddings stacked by bidirectional Long-short Term Memory and Conditional Random Fields for dealing with sequence labeling. Experimental results on a benchmark Google dataset show that by utilizing contextual embeddings, our model achieves a new state-of-theart F-score compared to strong methods reported on the leader board.