UniverSLU: A Versatile Spoken Language Understanding Model for Diverse Tasks with Natural Language Instructions

Core Concepts

A single multi-task learning model "UniverSLU" can perform various speech classification and sequence generation tasks, often outperforming or matching state-of-the-art task-specific models. UniverSLU leverages natural language instructions as prompts to enhance user-friendliness and generalization.

Abstract

The paper introduces a prompt-based multi-task learning (MTL) framework for spoken language understanding (SLU) tasks. The key contributions are: A novel approach that leverages human-interpretable natural language instructions combined with a list of option labels as prompts. This enhances the model's user-friendliness and ability to generalize to unseen paraphrases. Evaluation of the model's zero-shot capabilities, where it can generalize to new datasets and languages for seen task types, although it struggles with fully unseen task types. Building a single MTL model, named UniverSLU, that can perform 12 SLU task types across 17 datasets and 9 languages. UniverSLU often outperforms or matches state-of-the-art task-specific models. The paper starts by adapting a pre-trained automatic speech recognition model to additional SLU tasks using single-token task specifiers. It then enhances this approach through "instruction tuning", where the model is fine-tuned by describing the task using natural language instructions followed by the list of label options. The authors demonstrate the effectiveness of UniverSLU on a diverse set of SLU tasks, including speech classification (intent classification, speech command recognition, emotion recognition, etc.) and sequence generation (named entity recognition, semantic parsing). UniverSLU achieves competitive or superior performance compared to task-specific baselines on most tasks. The paper also investigates the model's zero-shot capabilities, finding that it can generalize to new datasets and languages for seen task types, but struggles with fully unseen task types. The authors discuss the potential limitations and future work to address these challenges.

Stats

"Our experiments encompass 10 speech classification and 2 sequence generation task types, covering 17 publicly available datasets based on 9 languages." "We demonstrate the efficacy of our single multi-task learning model "UniverSLU" for 12 speech classification and sequence generation task types spanning 17 datasets and 9 languages."

Quotes

"Recent studies leverage large language models with multi-tasking capabilities, using natural language prompts to guide the model's behavior and surpassing performance of task-specific models." "We demonstrate the efficacy of our single multi-task learning model "UniverSLU" for 12 speech classification and sequence generation task types spanning 17 datasets and 9 languages." "Our approach can generalize to new task descriptions for the seen tasks during inference, thereby enhancing its user-friendliness."

Key Insights Distilled From

UniverSLU

by Siddhant Aro... at arxiv.org 04-04-2024

https://arxiv.org/pdf/2310.02973.pdf

Deeper Inquiries

How can the UniverSLU model be further improved to generalize to completely new task types in a zero-shot manner?

To enhance the UniverSLU model's ability to generalize to completely new task types in a zero-shot manner, several strategies can be implemented: Few-shot Learning: Introduce a few-shot learning approach where the model is exposed to a small amount of labeled data for the new task types. This can help the model adapt quickly to unseen tasks by leveraging similarities with known tasks. Meta-Learning: Implement meta-learning techniques to enable the model to learn how to learn new tasks efficiently. By training the model on a variety of tasks and datasets, it can develop a meta-learning capability to generalize to new tasks. Task Agnostic Representations: Train the model to learn task-agnostic representations that capture the underlying structure of the data. By focusing on learning general features rather than task-specific ones, the model can better adapt to new tasks. Transfer Learning: Utilize transfer learning by pre-training the model on a diverse set of tasks and datasets. This can help the model extract useful features that are applicable to a wide range of tasks, facilitating zero-shot generalization. Adaptive Prompting: Develop a mechanism for adaptive prompting where the model can dynamically adjust its prompts based on the input data and task requirements. This flexibility can enable the model to handle new tasks more effectively.

How can the potential limitations of using a fixed list of options in the natural language instructions be addressed, and how can this be improved?

The potential limitations of using a fixed list of options in natural language instructions can be addressed through the following strategies: Dynamic Option Generation: Implement a mechanism to dynamically generate options based on the input data or context. This can ensure that the model is not constrained by a fixed set of options and can adapt to varying scenarios. Hierarchical Prompting: Introduce a hierarchical prompting approach where the model first predicts a high-level category and then generates specific options within that category. This hierarchical structure can provide more flexibility in handling diverse tasks. Attention Mechanisms: Incorporate attention mechanisms that allow the model to focus on relevant parts of the instruction and generate options accordingly. Attention can help the model dynamically adjust its predictions based on the input. Interactive Prompting: Enable interactive prompting where the model can interact with the user to clarify ambiguities or request additional information. This interactive approach can improve the accuracy of option generation in natural language instructions. Adversarial Training: Implement adversarial training techniques to enhance the robustness of the model in generating options. By exposing the model to adversarial examples during training, it can learn to generate more diverse and accurate options.

How can the UniverSLU model be adapted to handle non-speech audio tasks more effectively, given the current limitations observed on the audio classification task?

To improve the UniverSLU model's performance on non-speech audio tasks, especially considering the limitations observed on the audio classification task, the following strategies can be implemented: Fine-tuning on Audio Data: Fine-tune the model on a larger and more diverse audio dataset that includes non-speech audio samples. By exposing the model to a wider range of audio data, it can learn to better distinguish between different audio types. Data Augmentation: Apply data augmentation techniques specific to non-speech audio tasks, such as adding noise, pitch shifting, or time warping. Data augmentation can help the model generalize better to unseen variations in non-speech audio. Task-Specific Pre-training: Pre-train the model on a dataset specifically designed for non-speech audio tasks. By pre-training on relevant data, the model can learn task-specific features that are crucial for accurate classification. Multi-Modal Learning: Incorporate multi-modal learning by combining audio features with other modalities like text or images. By leveraging multiple sources of information, the model can gain a more comprehensive understanding of the audio data. Ensemble Learning: Implement ensemble learning techniques by combining predictions from multiple models trained on different aspects of non-speech audio tasks. Ensemble methods can improve the model's robustness and overall performance on complex tasks.