@inproceedings{kumar-etal-2020-device,
title = "On-Device detection of sentence completion for voice assistants with low-memory footprint",
author = "Kumar, Rahul and
Gour, Vijeta and
Pandey, Chandan and
Rao, Godawari Sudhakar and
Pai, Priyadarshini and
Bhasin, Anmol and
Samal, Ranjan",
editor = "Bhattacharyya, Pushpak and
Sharma, Dipti Misra and
Sangal, Rajeev",
booktitle = "Proceedings of the 17th International Conference on Natural Language Processing (ICON)",
month = dec,
year = "2020",
address = "Indian Institute of Technology Patna, Patna, India",
publisher = "NLP Association of India (NLPAI)",
url = "https://aclanthology.org/2020.icon-main.53",
pages = "384--392",
abstract = "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.",
}
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<abstract>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.</abstract>
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%0 Conference Proceedings
%T On-Device detection of sentence completion for voice assistants with low-memory footprint
%A Kumar, Rahul
%A Gour, Vijeta
%A Pandey, Chandan
%A Rao, Godawari Sudhakar
%A Pai, Priyadarshini
%A Bhasin, Anmol
%A Samal, Ranjan
%Y Bhattacharyya, Pushpak
%Y Sharma, Dipti Misra
%Y Sangal, Rajeev
%S Proceedings of the 17th International Conference on Natural Language Processing (ICON)
%D 2020
%8 December
%I NLP Association of India (NLPAI)
%C Indian Institute of Technology Patna, Patna, India
%F kumar-etal-2020-device
%X 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.
%U https://aclanthology.org/2020.icon-main.53
%P 384-392
Markdown (Informal)
[On-Device detection of sentence completion for voice assistants with low-memory footprint](https://aclanthology.org/2020.icon-main.53) (Kumar et al., ICON 2020)
ACL
- Rahul Kumar, Vijeta Gour, Chandan Pandey, Godawari Sudhakar Rao, Priyadarshini Pai, Anmol Bhasin, and Ranjan Samal. 2020. On-Device detection of sentence completion for voice assistants with low-memory footprint. In Proceedings of the 17th International Conference on Natural Language Processing (ICON), pages 384–392, Indian Institute of Technology Patna, Patna, India. NLP Association of India (NLPAI).