Few-shot multi-intent spoken language understanding (SLU) aims to detect user’s multiple intents and key slots using a tiny amount of annotated data. Prevailing multi-intent SLU models typically rely on abundant data for effective training, enabling them to capture corresponding relationships between intents and slots. However, in few-shot scenarios, establishing these connections becomes challenging, especially in situations involving multiple intents, which may result in confused relationships between intents and slots. To overcome the challenge, we propose decomposing the multi-label intent detection task into several single-label tasks, which reduces the complexity of model training while preserving the constraint relationships between intents and their related slots. We design description templates for each intent and respectively predict the correlation between the utterance and each intent description while completing the slot filling task under the corresponding intent. Therefore, each intent can independently guide the slot filling process, mitigating potentially confused relationships between multiple intents and slots. Experimental results on public datasets indicate that the performance of our model is better than ChatGPT and achieves state-of-the-art results.

Emotion-cause pair extraction (ECPE) aims to obtain all emotion-cause pairs consisting of the emotion clause and the corresponding cause clause in a document. Many existing works for ECPE utilize BERT to obtain representation on each clause in the document, and then perform the classification of Cartesian product among all clause representations or the clause-level sequence tagging. In this paper, we propose to redefine ECPE as the emotion-cause relationship between clauses prediction (ECRP). ECRP fits well with the form of the next sentence prediction task in BERT, which effectively unifies the BERT’s pre-training and the ECPE-specific fi ne-tuning process. According to the task form of ECRP, we reconstruct the original ECPE dataset from the document format to the clause-pair format. The scale of data is effectively expanded, and the imbalance of data is alleviated to a certain extent because some redundant data is filtered out based o n t he r elative distance between clauses. Experiments demonstrate that our ECRP-BERT model outperforms many competitive baselines. Especially in the case of low resources, the ECRP-BERT model still achieves a good performance.

At present, spoken language understanding (SLU) in multi-turn dialogue is a research hotspot, which mainly includes intent detection and slot filling. SLU models trained by large-scale corpus can learn good superficial semantic and grammatical information. But they lack the ability for modeling the knowledge needed to understand language. In order to further understand the deep semantic information of the dialogue, external knowledge needs to be modeled and incorporated into the SLU model. In addition, utilizing the correlation between history dialogue and current utterance is able to understand dialogue in multi-turn SLU. Thus, this paper proposes a joint model of intent detection and slot filling based on history context and external knowledge. This model constructs history dialogue encoder to obtain history context. Meanwhile, it constructs knowledge attention over context module. This module selects external knowledge according to the context information in current utterance and obtains knowledge representation. Finally, the history context and external knowledge representation are incorporated into the intent detection and slot filling joint model. The result of experiments on the common dataset demonstrate that with the help of external knowledge and history context, the performance of our model has a significant improvement.