Cross-domain slot filling is a widely explored problem in spoken language understanding (SLU), which requires the model to transfer between different domains under data sparsity conditions. Dominant two-step hierarchical models first extract slot entities and then calculate the similarity score between slot description-based prototypes and the last hidden layer of the slot entity, selecting the closest prototype as the predicted slot type. However, these models only use slot descriptions as prototypes, which lacks robustness. Moreover, these approaches have less regard for the inherent knowledge in the slot entity embedding to suffer from the issue of overfitting. In this letter, we propose a Robust Multi-prototypes Aware Integration (RMAI) method for zero-shot cross-domain slot filling. In RMAI, more robust slot entity-based prototypes and inherent knowledge in the slot entity embedding are utilized to improve the classification performance and alleviate the risk of overfitting. Furthermore, a multi-prototypes aware integration approach is proposed to effectively integrate both our proposed slot entity-based prototypes and the slot description-based prototypes. Experimental results on the SNIPS dataset demonstrate the well performance of RMAI.

Multi-intent spoken language understanding (SLU) that can handle multiple intents in an utterance has attracted increasing attention. Previous studies treat the slot filling task as a token-level sequence labeling task, which results in a lack of entity-related information. In our paper, we propose anEntity-LevelSlotFilling (ELSF) framework for joint multiple intent detection and slot filling. In our framework, two entity-oriented auxiliary tasks, entity boundary detection and entity type assignment, are introduced as the regularization to capture the entity boundary and the context of type, respectively. Besides, to better utilize the entity interaction, we design an effective entity-level coordination mechanism for modeling the interaction in both entity-entity and intent-entity relationships. Experiments on five datasets demonstrate the effectiveness and generalizability of our ELSF.

Intent detection and slot filling are two important tasks for spoken language understanding. Considering the close relation between them, most existing methods joint them by sharing parameters or establishing explicit connection between them for potentially benefiting each other. However, most of them only consider single directional connection and ignore their cross-impact between them. Moreover, these joint methods treat the predicted labels as the gold labels, which may cause error propagation. In this paper, we propose a two-stage Graph Attention Interactive Refine (GAIR) framework. In stage one, the basic SLU model predicts the coarse intent and slots. In stage two, we select the top-k candidate labels from stage one and construct a graph to make full advantage of intent and slot filling information. By constructing such graph, our framework can establish a bidirectional connection between two tasks and refine the coarse result, which can better take full use of cross-impact between two tasks. Moreover, contextual regularization is introduced for better alleviating error propagation. Experiments on two datasets show that our model achieves the state-of-the-arts performance.

Multi-intent spoken language understanding (SLU) that can handle an utterance containing multiple intents is more practical and attracts increasing attention. However, existing state-of-the-art models are either too coarse-grained (Utterance-level) or too fine-grained (Token-level) in intent detection, and thus may fail to recognize the intent transition point and the correct intents in an utterance. In this paper, we propose a Chunk-Level Intent Detection (CLID) framework, where we introduce a sliding window-based self-attention (SWSA) scheme for regional chunk intent detection. Based on the SWSA, an auxiliary task is introduced to identify the intent transition point in an utterance and obtain sub-utterances with a single intent. The intent of each sub-utterance is then predicted by assembling the intent predictions of the chunks (in a sliding window manner) within it. We conduct experiments on two public datasets, MixATIS and MixSNIPS, and the results show that our model achieves state-of-the-art performance.