Learning Spoken Language Representations with Neural Lattice Language Modeling
This work addresses the challenge of spoken language understanding for NLP applications, representing an incremental improvement by extending existing text-based methods to speech lattices.
The paper tackles the problem of adapting pre-trained language models to spoken language by proposing a neural lattice language modeling framework that reduces speech data demands and improves efficiency, achieving consistent performance gains over strong baselines on intent detection and dialogue act recognition datasets.
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.