Recent research has increasingly focused on multiple intent detection and slot filling tasks due to the closer to the complex multi-intent scenarios in the real world. However, existing approaches face two potential issues: (i) Single joint models are unable to capture full information and handle complex relations in data, and (ii) Irrelevant label relations can lead to over-guidance when conducting explicit interactions in multi-intent scenarios. To address the above issues, we propose a hybrid model based on Graph Convolutional Network and Multi-head Transformer (GCN-MT). This model alleviates over-guidance through a graph network at the entire corpus level and realizes intent-slot interaction through a co-attention network at both the specific utterance and local token levels. Experimental results demonstrate that our approach outperforms existing reproducible models on two public multi-intent datasets.

Time series forecasting has historically been a popular research area, attracting widespread interest in academia and industry. Recently, Deep Learning based models like RNN, LSTM, and NeuralProphet have been successfully applied for time series forecasting. However, single forecasting models are unable to capture full information and learn complex patterns in data. The combination of models has proved to be an effective strategy to address this issue. Traditional hybrid structures still limit the forecasting ability of hybrid methods. In this paper, we propose a novel hybrid forecasting model based on LSTM and NeuralProphet (NP-LSTM), which is constructed by a parallel-series hybrid structure. The proposed model uses LSTM to model diverse nonlinear relationships and NeuralProphet is designed to extract primary trends and seasonal effects and provide interpretability. In the experiments of this paper, we compare our model with other single models and hybrid models of traditional structures using four real-world datasets. The experimental results show that our NP-LSTM hybrid model obtains superior performance in various metrics for time series forecasting.