Warped Language Models for Noise Robust Language Understanding

TL;DR

This paper introduces Warped Language Models (WLM), which incorporate insertion and dropping of tokens during training to improve robustness of language understanding systems against speech recognition noise.

Contribution

The paper proposes a novel training method for language models that enhances robustness to ASR errors by simulating noise through token insertion and dropping.

Findings

WLM outperforms traditional MLM in noisy speech scenarios.

WLM shows improved accuracy in spoken language understanding tasks.

Training with noise-like modifications enhances model robustness.

Abstract

Masked Language Models (MLM) are self-supervised neural networks trained to fill in the blanks in a given sentence with masked tokens. Despite the tremendous success of MLMs for various text based tasks, they are not robust for spoken language understanding, especially for spontaneous conversational speech recognition noise. In this work we introduce Warped Language Models (WLM) in which input sentences at training time go through the same modifications as in MLM, plus two additional modifications, namely inserting and dropping random tokens. These two modifications extend and contract the sentence in addition to the modifications in MLMs, hence the word "warped" in the name. The insertion and drop modification of the input text during training of WLM resemble the types of noise due to Automatic Speech Recognition (ASR) errors, and as a result WLMs are likely to be more robust to ASR noise. Through computational results we show that natural language understanding systems built on top of WLMs perform better compared to those built based on MLMs, especially in the presence of ASR errors.