# A Discriminative Entity-Aware Language Model for Virtual Assistants

arXiv: https://arxiv.org/abs/2106.11292 · HTML: https://mlsanity.com/paper/2106.11292/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 15 references that are in the map.
- First posted on arXiv 21 Jun 2021.

## Builds on (most cited first, 5 of 5)

- [Barack's Wife Hillary: Using Knowledge-Graphs for Fact-Aware Language Modeling](https://mlsanity.com/paper/1906.07241/) (2019)
- [Latent Relation Language Models](https://mlsanity.com/paper/1908.07690/) (2019)
- [SNDCNN: Self-normalizing deep CNNs with scaled exponential linear units for speech recognition](https://mlsanity.com/paper/1910.01992/) (2019)
- [Scalable language model adaptation for spoken dialogue systems](https://mlsanity.com/paper/1812.04647/) (2018)
- [Predicting Entity Popularity to Improve Spoken Entity Recognition by Virtual Assistants](https://mlsanity.com/paper/2005.12816/) (2020)

## Built on by (most cited first, 7 of 7)

- [Training Large-Vocabulary Neural Language Models by Private Federated Learning for Resource-Constrained Devices](https://mlsanity.com/paper/2207.08988/) (2022)
- [Synthetic Query Generation using Large Language Models for Virtual Assistants](https://mlsanity.com/paper/2406.06729/) (2024)
- [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)
- [Record Deduplication for Entity Distribution Modeling in ASR Transcripts](https://mlsanity.com/paper/2306.06246/) (2023)
- [Auto Review: Second Stage Error Detection for Highly Accurate Information Extraction from Phone Conversations](https://mlsanity.com/paper/2506.05400/) (2025)
- [Phonetically-Augmented Discriminative Rescoring for Voice Search Error Correction](https://mlsanity.com/paper/2506.06117/) (2025)

## Abstract (arXiv)

High-quality automatic speech recognition (ASR) is essential for virtual assistants (VAs) to work well. However, ASR often performs poorly on VA requests containing named entities. In this work, we start from the observation that many ASR errors on named entities are inconsistent with real-world knowledge. We extend previous discriminative n-gram language modeling approaches to incorporate real-world knowledge from a Knowledge Graph (KG), using features that capture entity type-entity and entity-entity relationships. We apply our model through an efficient lattice rescoring process, achieving relative sentence error rate reductions of more than 25% on some synthesized test sets covering less popular entities, with minimal degradation on a uniformly sampled VA test set.