Spelling error correction is an important yet challenging task because a satisfactory solution of it essentially needs human-level language understanding ability. Without loss of generality we consider Chinese spelling error correction (CSC) in this paper. A state-ofthe-art method for the task selects a character from a list of candidates for correction (including non-correction) at each position of the sentence on the basis of BERT, the language representation model. The accuracy of the method can be sub-optimal, however, because BERT does not have sufficient capability to detect whether there is an error at each position, apparently due to the way of pre-training it using mask language modeling. In this work, we propose a novel neural architecture to address the aforementioned issue, which consists of a network for error detection and a network for error correction based on BERT, with the former being connected to the latter with what we call soft-masking technique. Our method of using 'Soft-Masked BERT' is general, and it may be employed in other language detectioncorrection problems. Experimental results on two datasets demonstrate that the performance of our proposed method is significantly better than the baselines including the one solely based on BERT.

Spelling error correction is an important yet challenging task because a satisfactory solution of it essentially needs human-level language understanding ability. Without loss of generality we consider Chinese spelling error correction (CSC) in this paper. A state-ofthe-art method for the task selects a character from a list of candidates for correction (including non-correction) at each position of the sentence on the basis of BERT, the language representation model. The accuracy of the method can be sub-optimal, however, because BERT does not have sufficient capability to detect whether there is an error at each position, apparently due to the way of pre-training it using mask language modeling. In this work, we propose a novel neural architecture to address the aforementioned issue, which consists of a network for error detection and a network for error correction based on BERT, with the former being connected to the latter with what we call soft-masking technique. Our method of using 'Soft-Masked BERT' is general, and it may be employed in other language detectioncorrection problems. Experimental results on two datasets demonstrate that the performance of our proposed method is significantly better than the baselines including the one solely based on BERT.

Prototyping is an efficient and effective way to understand and validate system requirements at the early stage of software development. In this paper, we present an approach for transforming UML system requirement models with OCL specifications into executable prototypes with the function of checking multiplicity and invariant constraints. Generally, a use case in UML can be described as a sequence of system operations. A system operation can be formally defined by a pair of preconditions and postconditions specified using OCL in the context of the conceptual class model. By analyzing the semantics of the preconditions and postconditions, the execution of the operation can be prototyped as a sequence of primitive actions which first check the precondition, and then enforce the postcondition by transferring the system from a pre-state to a post-state step by step. The primitive actions are basic manipulations of the system state (an object diagram), including find objects and links, create and remove objects and links, and check and set attribute values. Based on this approach, we have developed a tool of automatic prototype generation and analysis: AutoPA3.0.