# Contextual Language Model Adaptation for Conversational Agents

arXiv: https://arxiv.org/abs/1806.10215 · HTML: https://mlsanity.com/paper/1806.10215/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Jun 2018.

## Built on by (most cited first, 10 of 10)

- [Tree-constrained Pointer Generator for End-to-end Contextual Speech Recognition](https://mlsanity.com/paper/2109.00627/) (2021)
- [Retrieval Augmented End-to-End Spoken Dialog Models](https://mlsanity.com/paper/2402.01828/) (2024)
- [Tree-constrained Pointer Generator with Graph Neural Network Encodings for Contextual Speech Recognition](https://mlsanity.com/paper/2207.00857/) (2022)
- [Domain-aware Neural Language Models for Speech Recognition](https://mlsanity.com/paper/2101.03229/) (2021)
- [Adapting Long Context NLM for ASR Rescoring in Conversational Agents](https://mlsanity.com/paper/2104.11070/) (2021)
- [LLM-based speaker diarization correction: A generalizable approach](https://mlsanity.com/paper/2406.04927/) (2024)
- [ASR Adaptation for E-commerce Chatbots using Cross-Utterance Context and Multi-Task Language Modeling](https://mlsanity.com/paper/2106.09532/) (2021)
- [Attentive Contextual Carryover for Multi-Turn End-to-End Spoken Language Understanding](https://mlsanity.com/paper/2112.06743/) (2021)
- [Contextual Biasing of Language Models for Speech Recognition in Goal-Oriented Conversational Agents](https://mlsanity.com/paper/2103.10325/) (2021)
- [Building English ASR model with regional language support](https://mlsanity.com/paper/2503.07522/) (2025)

## Abstract (arXiv)

Statistical language models (LM) play a key role in Automatic Speech Recognition (ASR) systems used by conversational agents. These ASR systems should provide a high accuracy under a variety of speaking styles, domains, vocabulary and argots. In this paper, we present a DNN-based method to adapt the LM to each user-agent interaction based on generalized contextual information, by predicting an optimal, context-dependent set of LM interpolation weights. We show that this framework for contextual adaptation provides accuracy improvements under different possible mixture LM partitions that are relevant for both (1) Goal-oriented conversational agents where it's natural to partition the data by the requested application and for (2) Non-goal oriented conversational agents where the data can be partitioned using topic labels that come from predictions of a topic classifier. We obtain a relative WER improvement of 3% with a 1-pass decoding strategy and 6% in a 2-pass decoding framework, over an unadapted model. We also show up to a 15% relative improvement in recognizing named entities which is of significant value for conversational ASR systems.