Bidirectional Representations for Low Resource Spoken Language Understanding

• URL: http://arxiv.org/abs/2211.14320v2
• Date: Sat, 14 Oct 2023 13:18:26 GMT
• Title: Bidirectional Representations for Low Resource Spoken Language Understanding
• Authors: Quentin Meeus, Marie-Francine Moens, Hugo Van hamme
• Abstract summary: We propose a representation model to encode speech in bidirectional rich encodings. The approach uses a masked language modelling objective to learn the representations. We show that the performance of the resulting encodings is better than comparable models on multiple datasets.
• Score: 39.208462511430554
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Most spoken language understanding systems use a pipeline approach composed of an automatic speech recognition interface and a natural language understanding module. This approach forces hard decisions when converting continuous inputs into discrete language symbols. Instead, we propose a representation model to encode speech in rich bidirectional encodings that can be used for downstream tasks such as intent prediction. The approach uses a masked language modelling objective to learn the representations, and thus benefits from both the left and right contexts. We show that the performance of the resulting encodings before fine-tuning is better than comparable models on multiple datasets, and that fine-tuning the top layers of the representation model improves the current state of the art on the Fluent Speech Command dataset, also in a low-data regime, when a limited amount of labelled data is used for training. Furthermore, we propose class attention as a spoken language understanding module, efficient both in terms of speed and number of parameters. Class attention can be used to visually explain the predictions of our model, which goes a long way in understanding how the model makes predictions. We perform experiments in English and in Dutch.

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