@inproceedings{zhu-etal-2023-efficient,
title = "An Efficient Conversational Smart Compose System",
author = "Zhu, Yun and
Chen, Xiayu and
Shu, Lei and
Tan, Bowen and
Song, Xinying and
Liu, Lijuan and
Wang, Maria and
Chen, Jindong and
Ruan, Ning",
editor = "Bollegala, Danushka and
Huang, Ruihong and
Ritter, Alan",
booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations)",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.acl-demo.43",
doi = "10.18653/v1/2023.acl-demo.43",
pages = "456--462",
abstract = "Online conversation is a ubiquitous way to share information and connect everyone but repetitive idiomatic text typing takes users a lot of time. This paper demonstrates a simple yet effective cloud based smart compose system to improve human-to-human conversation efficiency. Heuristics from different perspectives are designed to achieve the best trade-off between quality and latency. From the modeling side, the decoder-only model exploited the previous turns of conversational history in a computation lightweight manner. Besides, a novel phrase tokenizer is proposed to reduce latency without losing the composing quality further. Additionally, the caching mechanism is applied to the serving framework. The demo video of the system is available at \url{https://youtu.be/U1KXkaqr60g.We} open-sourced our phrase tokenizer in \url{https://github.com/tensorflow/text}.",
}
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<abstract>Online conversation is a ubiquitous way to share information and connect everyone but repetitive idiomatic text typing takes users a lot of time. This paper demonstrates a simple yet effective cloud based smart compose system to improve human-to-human conversation efficiency. Heuristics from different perspectives are designed to achieve the best trade-off between quality and latency. From the modeling side, the decoder-only model exploited the previous turns of conversational history in a computation lightweight manner. Besides, a novel phrase tokenizer is proposed to reduce latency without losing the composing quality further. Additionally, the caching mechanism is applied to the serving framework. The demo video of the system is available at https://youtu.be/U1KXkaqr60g.We open-sourced our phrase tokenizer in https://github.com/tensorflow/text.</abstract>
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%0 Conference Proceedings
%T An Efficient Conversational Smart Compose System
%A Zhu, Yun
%A Chen, Xiayu
%A Shu, Lei
%A Tan, Bowen
%A Song, Xinying
%A Liu, Lijuan
%A Wang, Maria
%A Chen, Jindong
%A Ruan, Ning
%Y Bollegala, Danushka
%Y Huang, Ruihong
%Y Ritter, Alan
%S Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations)
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F zhu-etal-2023-efficient
%X Online conversation is a ubiquitous way to share information and connect everyone but repetitive idiomatic text typing takes users a lot of time. This paper demonstrates a simple yet effective cloud based smart compose system to improve human-to-human conversation efficiency. Heuristics from different perspectives are designed to achieve the best trade-off between quality and latency. From the modeling side, the decoder-only model exploited the previous turns of conversational history in a computation lightweight manner. Besides, a novel phrase tokenizer is proposed to reduce latency without losing the composing quality further. Additionally, the caching mechanism is applied to the serving framework. The demo video of the system is available at https://youtu.be/U1KXkaqr60g.We open-sourced our phrase tokenizer in https://github.com/tensorflow/text.
%R 10.18653/v1/2023.acl-demo.43
%U https://aclanthology.org/2023.acl-demo.43
%U https://doi.org/10.18653/v1/2023.acl-demo.43
%P 456-462
Markdown (Informal)
[An Efficient Conversational Smart Compose System](https://aclanthology.org/2023.acl-demo.43) (Zhu et al., ACL 2023)
ACL
- Yun Zhu, Xiayu Chen, Lei Shu, Bowen Tan, Xinying Song, Lijuan Liu, Maria Wang, Jindong Chen, and Ning Ruan. 2023. An Efficient Conversational Smart Compose System. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations), pages 456–462, Toronto, Canada. Association for Computational Linguistics.