This paper synthesizes the findings of application carried out in recent years and looks at the influence of automatic robots on financial scenario. Robot autonomous sorting can greatly improve the efficiency of perform tasks that require manual labor and free financial staffs from simple and single repetition work. It is estimated that robots are 15 times more efficient than human and the comprehensive cost is only 8% based on three-year workload. Starts from this background, a systematic description was carried out to analyze the use of dynamic cooperative robots in financial bill recognition and classification, while the opportunities, potential benefits and challenges to implementing dynamic cooperative robots in financial work was also discussed. The dynamic cooperative robot is consisted of mechanical arm, bill recognition system, cloud processing server and support multiple areas financial work. This work is the first to identify and discuss how financial work processed with all automatic systems and serves as a catalyst to spur future research in financial bill processing area.

Design of Dynamic Cooperative Robot System for Financial Bill Recognition

String (word) embeddings are keys to advanced neural natural language understanding (NLU) models. Recently, a new embedding called contextual string embedding (CtxEmb) reached a new state of the art on many NLU tasks. Specially, a pooling variant of CtxEmb demonstrated new best results on named entity recognition (NER) tasks. While the pooling variant is good at reaching new best performance in terms of Micro F1 score, it poses questions on what kind of information in the pool is helpful. In particular, the pooling variant maintains a (possibly long) list of previously seen occurrences of a word, and computes embedding for the word in a new context based on those occurrences. In this paper, we propose a strategy to forget some previous occurrences, and thus exploring which history are beneficial in constructing embeddings for a new context of a word. The proposed forgotten strategy is designed by accounting for a distance metric defined in this paper. Preliminary experiments on the WNUT-17 task show the effectiveness of our forgotten strategy, and uncover that embeddings that are diverse in terms of cosine similarity are helpful in forming an aggregated embedding.

A Forgotten Strategy for Pooled Contextualized Embedding Learning

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Design of Dynamic Cooperative Robot System for Financial Bill Recognition

A Forgotten Strategy for Pooled Contextualized Embedding Learning