Tie Your Embeddings Down: Cross-Modal Latent Spaces for End-to-end Spoken Language Understanding
This work provides an incremental improvement in end-to-end spoken language understanding, particularly beneficial for scenarios with limited training data.
This paper addresses the challenge of training end-to-end spoken language understanding (SLU) systems with limited audio-semantics data by using a cross-modal latent space architecture. The proposed triplet loss function, guiding acoustic embeddings closer to text embeddings from a pre-trained BERT model, achieved relative improvements of 1.4% and 4% over an E2E model without a cross-modal space, and 0.7% and 1% over a previous CMLS model using L2 loss.
End-to-end (E2E) spoken language understanding (SLU) systems can infer the semantics of a spoken utterance directly from an audio signal. However, training an E2E system remains a challenge, largely due to the scarcity of paired audio-semantics data. In this paper, we treat an E2E system as a multi-modal model, with audio and text functioning as its two modalities, and use a cross-modal latent space (CMLS) architecture, where a shared latent space is learned between the `acoustic' and `text' embeddings. We propose using different multi-modal losses to explicitly guide the acoustic embeddings to be closer to the text embeddings, obtained from a semantically powerful pre-trained BERT model. We train the CMLS model on two publicly available E2E datasets, across different cross-modal losses and show that our proposed triplet loss function achieves the best performance. It achieves a relative improvement of 1.4% and 4% respectively over an E2E model without a cross-modal space and a relative improvement of 0.7% and 1% over a previously published CMLS model using $L_2$ loss. The gains are higher for a smaller, more complicated E2E dataset, demonstrating the efficacy of using an efficient cross-modal loss function, especially when there is limited E2E training data available.