# Predicting Entity Popularity to Improve Spoken Entity Recognition by Virtual Assistants

arXiv: https://arxiv.org/abs/2005.12816 · HTML: https://mlsanity.com/paper/2005.12816/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 May 2020.

## Built on by (most cited first, 8 of 8)

- [Synthetic Query Generation using Large Language Models for Virtual Assistants](https://mlsanity.com/paper/2406.06729/) (2024)
- [Error-driven Pruning of Language Models for Virtual Assistants](https://mlsanity.com/paper/2102.07219/) (2021)
- [A Discriminative Entity-Aware Language Model for Virtual Assistants](https://mlsanity.com/paper/2106.11292/) (2021)
- [Space-Efficient Representation of Entity-centric Query Language Models](https://mlsanity.com/paper/2206.14885/) (2022)
- [Modeling Spoken Information Queries for Virtual Assistants: Open Problems, Challenges and Opportunities](https://mlsanity.com/paper/2304.13149/) (2023)
- [Server-side Rescoring of Spoken Entity-centric Knowledge Queries for Virtual Assistants](https://mlsanity.com/paper/2311.01398/) (2023)
- [Record Deduplication for Entity Distribution Modeling in ASR Transcripts](https://mlsanity.com/paper/2306.06246/) (2023)
- [Image-Seeking Intent Prediction for Cross-Device Product Search](https://mlsanity.com/paper/2511.14764/) (2025)

## Abstract (arXiv)

We focus on improving the effectiveness of a Virtual Assistant (VA) in recognizing emerging entities in spoken queries. We introduce a method that uses historical user interactions to forecast which entities will gain in popularity and become trending, and it subsequently integrates the predictions within the Automated Speech Recognition (ASR) component of the VA. Experiments show that our proposed approach results in a 20% relative reduction in errors on emerging entity name utterances without degrading the overall recognition quality of the system.