Abstract: Automated development of an electronic data interchange (EDI) translator includes mapping, by a first machine learning model, source fields of documents formatted according to a first EDI format to destination fields of documents formatted according to a second EDI format. A mapping requirements specification is translated by a second machine learning model into code executable by a computer processor. A translation object is generated based on the mapping and translating. The translation object is used by an EDI translator that translates documents formatted according to the first EDI format into documents formatted according to the second EDI format is generated based on the mapping and translating.

Abstract: Multi-modal representation learning for electronic data interchange (EDI) and text includes encoding a text sample with a text encoder. The encoding creates an embedded text sample in a conjoined embedding space. An EDI sample is encoded with a multi-model EDI encoder. Encoding the EDI sample creates an embedded EDI sample in the conjoined embedding space. The multi-model EDI encoder includes a plurality of expert networks. The multi-modal EDI encoder is trained using machine learning to determine a similarity score between a target text and an EDI segment. The machine learning includes comparing labeled training samples and corresponding predictions generated by the multi-model EDI encoder. Once trained, the multi-model EDI encoder is configured to query a data repository of EDI documents for one or more selected EDI documents that match a natural language text input.

Abstract: Mechanisms are provided for generating electronic data interchange mapping source code. A source code generator, comprising a machine learning trained neural network computer model, receives input data comprising a source document data structure in a first format and a destination document data structure in a second format, different from the first format. The source code generator processes the input data to generate a source code sequence output vector. A source code is generated that maps contents of source documents in the first format to contents of destination documents in the second format based on the source code sequence output vector. The source code is output to a computing device for execution on other source documents that utilize the first format to thereby automatically generate corresponding destination documents in the second format.

Abstract: Mechanisms are provided for generating electronic data interchange mapping source code. A source code generator, comprising a machine learning trained neural network computer model, receives input data comprising a source document data structure in a first format and a destination document data structure in a second format, different from the first format. The source code generator processes the input data to generate a source code sequence output vector. A source code is generated that maps contents of source documents in the first format to contents of destination documents in the second format based on the source code sequence output vector. The source code is output to a computing device for execution on other source documents that utilize the first format to thereby automatically generate corresponding destination documents in the second format.