Sentence completion detection (SCD) is an important task for various downstream Natural Language Processing (NLP) based applications. For NLP based applications, which use the Automatic Speech Recognition (ASR) from third parties as a service, SCD is essential to prevent unnecessary processing. Conventional approaches for SCD operate within the confines of sentence boundary detection using language models or sentence end detection using speech and text features. These have limitations in terms of relevant available data for training, performance within the memory and latency constraints, and the generalizability across voice assistant domains. In this paper, we propose a novel sentence completion detection method with low memory footprint for On-Device applications. We explore various sequence-level and sentence-level experiments using state-of-the-art Bi-LSTM and BERT based models for English language.

Determining the popularity of a Named Entity after completion of Named Entity Recognition (NER) task finds many applications. This work studies Named Entities of Music and Movie domains and solves the problem considering relevant 11 features. Decision Trees and Random Forests approaches were applied on the dataset and the latter algorithm resulted in acceptable accuracy.