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Complete Guide to ENCONTER: Entity Constrained Insertion Transformer for Language Modelling

SMU Lee Kong Chian Professor of Information Systems and Director of the SMU Living Analytics Research Centre Lim Ee Peng, SMU Research Engineer Hsieh Lee-Hsun and SMU Research Fellow Lee Yang-Yin have introduced a successful model, the “Entity Constrained Insertion Transformer”, shortly known as the ENCONTER that addresses the problem of entity-constrained text generation. ENCONTER is able to generate a job description when entities such as candidate’s desired skills and roles are provided. It can generate a recipe when entities such as ingredients and their quantities are provided, as well as generate a poem when entities such as keywords are provided.