@inproceedings{tang-etal-2021-voice,
title = "Voice Query Auto Completion",
author = "Tang, Raphael and
Kumar, Karun and
Chalkley, Kendra and
Xin, Ji and
Zhang, Liming and
Li, Wenyan and
Yang, Gefei and
Mao, Yajie and
Shin, Junho and
Murray, Geoffrey Craig and
Lin, Jimmy",
editor = "Moens, Marie-Francine and
Huang, Xuanjing and
Specia, Lucia and
Yih, Scott Wen-tau",
booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2021",
address = "Online and Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.emnlp-main.68/",
doi = "10.18653/v1/2021.emnlp-main.68",
pages = "900--906",
abstract = "Query auto completion (QAC) is the task of predicting a search engine user`s final query from their intermediate, incomplete query. In this paper, we extend QAC to the streaming voice search setting, where automatic speech recognition systems produce intermediate transcriptions as users speak. Naively applying existing methods fails because the intermediate transcriptions often don`t form prefixes or even substrings of the final transcription. To address this issue, we propose to condition QAC approaches on intermediate transcriptions to complete voice queries. We evaluate our models on a speech-enabled smart television with real-life voice search traffic, finding that this ASR-aware conditioning improves the completion quality. Our best method obtains an 18{\%} relative improvement in mean reciprocal rank over previous methods."
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<abstract>Query auto completion (QAC) is the task of predicting a search engine user‘s final query from their intermediate, incomplete query. In this paper, we extend QAC to the streaming voice search setting, where automatic speech recognition systems produce intermediate transcriptions as users speak. Naively applying existing methods fails because the intermediate transcriptions often don‘t form prefixes or even substrings of the final transcription. To address this issue, we propose to condition QAC approaches on intermediate transcriptions to complete voice queries. We evaluate our models on a speech-enabled smart television with real-life voice search traffic, finding that this ASR-aware conditioning improves the completion quality. Our best method obtains an 18% relative improvement in mean reciprocal rank over previous methods.</abstract>
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%0 Conference Proceedings
%T Voice Query Auto Completion
%A Tang, Raphael
%A Kumar, Karun
%A Chalkley, Kendra
%A Xin, Ji
%A Zhang, Liming
%A Li, Wenyan
%A Yang, Gefei
%A Mao, Yajie
%A Shin, Junho
%A Murray, Geoffrey Craig
%A Lin, Jimmy
%Y Moens, Marie-Francine
%Y Huang, Xuanjing
%Y Specia, Lucia
%Y Yih, Scott Wen-tau
%S Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
%D 2021
%8 November
%I Association for Computational Linguistics
%C Online and Punta Cana, Dominican Republic
%F tang-etal-2021-voice
%X Query auto completion (QAC) is the task of predicting a search engine user‘s final query from their intermediate, incomplete query. In this paper, we extend QAC to the streaming voice search setting, where automatic speech recognition systems produce intermediate transcriptions as users speak. Naively applying existing methods fails because the intermediate transcriptions often don‘t form prefixes or even substrings of the final transcription. To address this issue, we propose to condition QAC approaches on intermediate transcriptions to complete voice queries. We evaluate our models on a speech-enabled smart television with real-life voice search traffic, finding that this ASR-aware conditioning improves the completion quality. Our best method obtains an 18% relative improvement in mean reciprocal rank over previous methods.
%R 10.18653/v1/2021.emnlp-main.68
%U https://aclanthology.org/2021.emnlp-main.68/
%U https://doi.org/10.18653/v1/2021.emnlp-main.68
%P 900-906
Markdown (Informal)
[Voice Query Auto Completion](https://aclanthology.org/2021.emnlp-main.68/) (Tang et al., EMNLP 2021)
ACL
- Raphael Tang, Karun Kumar, Kendra Chalkley, Ji Xin, Liming Zhang, Wenyan Li, Gefei Yang, Yajie Mao, Junho Shin, Geoffrey Craig Murray, and Jimmy Lin. 2021. Voice Query Auto Completion. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 900–906, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics.