# Space-Efficient Representation of Entity-centric Query Language Models

arXiv: https://arxiv.org/abs/2206.14885 · HTML: https://mlsanity.com/paper/2206.14885/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 28 references that are in the map.
- First posted on arXiv 29 Jun 2022.

## Builds on (most cited first, 8 of 8)

- [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)
- [Connecting and Comparing Language Model Interpolation Techniques](https://mlsanity.com/paper/1908.09738/) (2019)
- [On-device neural speech synthesis](https://mlsanity.com/paper/2109.08710/) (2021)
- [Predicting Entity Popularity to Improve Spoken Entity Recognition by Virtual Assistants](https://mlsanity.com/paper/2005.12816/) (2020)
- [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)

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

- [Modeling Spoken Information Queries for Virtual Assistants: Open Problems, Challenges and Opportunities](https://mlsanity.com/paper/2304.13149/) (2023)
- [DeRAGEC: Denoising Named Entity Candidates with Synthetic Rationale for ASR Error Correction](https://mlsanity.com/paper/2506.07510/) (2025)
- [Transformer-based Model for ASR N-Best Rescoring and Rewriting](https://mlsanity.com/paper/2406.08207/) (2024)
- [Server-side Rescoring of Spoken Entity-centric Knowledge Queries for Virtual Assistants](https://mlsanity.com/paper/2311.01398/) (2023)
- [Towards Personalization of CTC Speech Recognition Models with Contextual Adapters and Adaptive Boosting](https://mlsanity.com/paper/2210.09510/) (2022)
- [Neural Language Model Pruning for Automatic Speech Recognition](https://mlsanity.com/paper/2310.03424/) (2023)
- [Phonetically-Augmented Discriminative Rescoring for Voice Search Error Correction](https://mlsanity.com/paper/2506.06117/) (2025)

## Abstract (arXiv)

Virtual assistants make use of automatic speech recognition (ASR) to help users answer entity-centric queries. However, spoken entity recognition is a difficult problem, due to the large number of frequently-changing named entities. In addition, resources available for recognition are constrained when ASR is performed on-device. In this work, we investigate the use of probabilistic grammars as language models within the finite-state transducer (FST) framework. We introduce a deterministic approximation to probabilistic grammars that avoids the explicit expansion of non-terminals at model creation time, integrates directly with the FST framework, and is complementary to n-gram models. We obtain a 10% relative word error rate improvement on long tail entity queries compared to when a similarly-sized n-gram model is used without our method.