Voice Query Auto Completion
Raphael Tang, Karun Kumar, Kendra Chalkley, Ji Xin, Liming Zhang, Wenyan Li, Gefei Yang, Yajie Mao, Junho Shin, Geoffrey Craig Murray, Jimmy Lin
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.- Anthology ID:
- 2021.emnlp-main.68
- Volume:
- Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
- Month:
- November
- Year:
- 2021
- Address:
- Online and Punta Cana, Dominican Republic
- Editors:
- Marie-Francine Moens, Xuanjing Huang, Lucia Specia, Scott Wen-tau Yih
- Venue:
- EMNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 900–906
- Language:
- URL:
- https://aclanthology.org/2021.emnlp-main.68
- DOI:
- 10.18653/v1/2021.emnlp-main.68
- Cite (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.
- Cite (Informal):
- Voice Query Auto Completion (Tang et al., EMNLP 2021)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-3/2021.emnlp-main.68.pdf