On-Device detection of sentence completion for voice assistants with low-memory footprint

Rahul Kumar, Vijeta Gour, Chandan Pandey, Godawari Sudhakar Rao, Priyadarshini Pai, Anmol Bhasin, Ranjan Samal
2020 International Conference on Natural Language Processing  
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
more » ... f 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.
dblp:conf/icon-nlp/KumarGPRPBS20 fatcat:rk3mqbysgfe4hlyjuk2gqtju6u