Space-Efficient Representation of Entity-centric Query Language Models

• URL: http://arxiv.org/abs/2206.14885v1
• Date: Wed, 29 Jun 2022 19:59:50 GMT
• Title: Space-Efficient Representation of Entity-centric Query Language Models
• Authors: Christophe Van Gysel, Mirko Hannemann, Ernest Pusateri, Youssef Oualil, Ilya Oparin
• Abstract summary: We introduce a deterministic approximation to probabilistic grammars that avoids the explicit expansion of non-terminals at model creation time. 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.
• Score: 8.712427362992237
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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