Self Learning and Context based approach of Slot Label Creation and Slot Filling for Interactive Systems

Abstract

Abstract Slot-filling is considered one of the most important components of task-based interactive systems. The main challenge in slot filling is to assign a semantic label to each text in the utterance which is further useful for generating the response by the interactive system. Another challenge is the non-availability of extensive labeled data. Thus in this research, we primarily focus on developing two approaches namely (i) automatic slot identification through IOB labeling for slot labels creation and preparing a labeled dataset (ii) a self-learning based model where the slot labels are created in step (i) are predicted for an untrained query from the same or different domains. We use BI-LSTM with Elmo Embedding for our proposed frameworks with POS Tags to generate slot-independent tags and label descriptions for slot labeling. We applied and compared our model on the SNIPS and Mobile service provider dataset through the F1 score and analyzed that the efficiency of self-learning-based models highly depends upon the quality and correlation among different domains of the same dataset.