Space-Efficient Representation of Entity-centric Query Language Models

2022/06/29 by Van Gysel, Christophe, Hannemann, Mirko, Pusateri, Ernest +2 · 2 citations
#Artificial Intelligence (cs.AI) #Computation and Language (cs.CL) #FOS: Computer and information sciences

paper · doi:10.48550/arxiv.2206.14885

Abstract

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.

Cited by

• DeRAGEC: Denoising Named Entity Candidates with Synthetic Rationale for ASR Error Correction
• Phonetically-Augmented Discriminative Rescoring for Voice Search Error Correction

Related

• Server-side Rescoring of Spoken Entity-centric Knowledge Queries for Virtual Assistants
• Probabilistic top-down parsing and language modeling
• Leveraging Large Language Models for Exploiting ASR Uncertainty
• Retrieval Augmented Correction of Named Entity Speech Recognition Errors
• Exploiting Syntactic Structure for Natural Language Modeling