A Stack-Propagation Framework with Token-Level Intent Detection for Spoken Language Understanding (1909.02188v1)

Abstract: Intent detection and slot filling are two main tasks for building a spoken language understanding (SLU) system. The two tasks are closely tied and the slots often highly depend on the intent. In this paper, we propose a novel framework for SLU to better incorporate the intent information, which further guides the slot filling. In our framework, we adopt a joint model with Stack-Propagation which can directly use the intent information as input for slot filling, thus to capture the intent semantic knowledge. In addition, to further alleviate the error propagation, we perform the token-level intent detection for the Stack-Propagation framework. Experiments on two publicly datasets show that our model achieves the state-of-the-art performance and outperforms other previous methods by a large margin. Finally, we use the Bidirectional Encoder Representation from Transformer (BERT) model in our framework, which further boost our performance in SLU task.

A Stack-Propagation Framework with Token-Level Intent Detection for Spoken Language Understanding

The paper "A Stack-Propagation Framework with Token-Level Intent Detection for Spoken Language Understanding" presents an innovative approach to enhancing spoken language understanding (SLU) systems by integrating intent detection more directly with slot filling tasks. The research introduces a model leveraging Stack-Propagation to incorporate intent information explicitly into the slot filling process, thereby addressing interdependencies that are often apparent in practical applications but challenging to model efficiently.

Theoretical Framework and Methodology

The core proposition of the paper is the use of a joint model facilitated by Stack-Propagation, which allows the dynamic utilization of intent information at the token level, thus directly influencing the slot filling task. Traditional SLU systems separately handle intent detection—a classification task aimed at identifying user intent—and slot filling, a sequence labeling task extracting semantic components from spoken utterances. However, these two tasks have inherent dependencies where the identification of slots often relies on accurate intent detection.

The authors implement a self-attentive encoder, improving contextual feature representation through BiLSTM and self-attention mechanisms. They further address error propagation—a common challenge in SLU—by executing token-level intent detection, which offers a more granular integration of intent knowledge throughout the sequence. This multifunctional approach is designed to capture richer semantic interrelations between intended actions and slot representations.

Experimental Validation

The framework's effectiveness is validated against established SLU benchmarks such as SNIPS and ATIS. The experiments demonstrate considerable performance improvements across all SLU metrics, with the proposed model outstripping previous state-of-the-art solutions. Notably, the model achieves an F1 score enhancement for slot filling, improved accuracy in intent detection, and superior overall performance metrics for sentence-level semantic parsing, signifying an accurate integration of tasks.

A critical distinction in the paper, highlighted through comparative experiments, is the model's robustness against pipeline models or those employing indirect intent-slot relationships through gate mechanisms. By incorporating intent information directly and employing a token-level resolution, the Stack-Propagation approach reduces error propagation and misunderstanding between intent and slot predictions, thus ensuring a more fluid and contextual understanding of spoken language.

Implications and Future Directions

Practically, the findings suggest enhanced user interaction in dialogue systems by efficiently understanding and extracting user intentions and relevant data, which is elemental in improving the experience and accuracy of task-oriented dialogue interfaces. Theoretically, the introduction of token-level intent detection presents a nuanced way to look at multi-task learning within language understanding systems, potentially spurring further research into granular interaction in neural network models.

The paper also presents the implications of integrating Bidirectional Encoder Representations from Transformers (BERT) into their model, providing a pathway to further boost SLU tasks' performance by leveraging recent advances in pre-trained LLMs. The use of BERT, as demonstrated, significantly enhances both slot filling and intent detection, indicating potential avenues for future enhancements.

Overall, the Stack-Propagation framework presents substantial contributions to the SLU landscape, offering substantial performance improvements and novel methodologies that emphasize token-level intent detection and direct interactions between language understanding components. Future developments could explore the application of this framework across different languages and scenarios, adapating to evolving dialogue system requirements and cross-domain applications.