# Learning Spoken Language Representations with Neural Lattice Language Modeling

arXiv: https://arxiv.org/abs/2007.02629 · HTML: https://mlsanity.com/paper/2007.02629/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Jul 2020.

## Built on by (most cited first, 6 of 6)

- ["How Robust r u?": Evaluating Task-Oriented Dialogue Systems on Spoken Conversations](https://mlsanity.com/paper/2109.13489/) (2021)
- [Speech-language Pre-training for End-to-end Spoken Language Understanding](https://mlsanity.com/paper/2102.06283/) (2021)
- [Pre-training for Spoken Language Understanding with Joint Textual and Phonetic Representation Learning](https://mlsanity.com/paper/2104.10357/) (2021)
- [Contrastive Learning for Improving ASR Robustness in Spoken Language Understanding](https://mlsanity.com/paper/2205.00693/) (2022)
- [End-to-End Speech to Intent Prediction to improve E-commerce Customer Support Voicebot in Hindi and English](https://mlsanity.com/paper/2211.07710/) (2022)
- [Building Robust Spoken Language Understanding by Cross Attention between Phoneme Sequence and ASR Hypothesis](https://mlsanity.com/paper/2203.12067/) (2022)

## Abstract (arXiv)

Pre-trained language models have achieved huge improvement on many NLP tasks. However, these methods are usually designed for written text, so they do not consider the properties of spoken language. Therefore, this paper aims at generalizing the idea of language model pre-training to lattices generated by recognition systems. We propose a framework that trains neural lattice language models to provide contextualized representations for spoken language understanding tasks. The proposed two-stage pre-training approach reduces the demands of speech data and has better efficiency. Experiments on intent detection and dialogue act recognition datasets demonstrate that our proposed method consistently outperforms strong baselines when evaluated on spoken inputs. The code is available at https://github.com/MiuLab/Lattice-ELMo.