# Semantically Guided Enhanced Fusion for Intent Detection and Slot Filling
## 2 Related Work  
In natural language understanding tasks of IDSF, modeling the closely correlated relationship between the two tasks has been a key focus in many state-of-the-art joint models. These joint modeling approaches can be broadly classified into two categories. The first category involves the unidirectional utilization of intent labels as supplementary information to enhance the effectiveness of slot filling tasks. For instance, [PERSON] and [PERSON] [6] employed an intent-augmented gate mechanism for the slot filling task. [PERSON] and [PERSON] [10] pioneered the application of RNN in the task of intent detection. [PERSON] et al. [2] employed intent labels to guide the slot gate. [PERSON] et al. [3] leveraged intent semantic representations as gate markers for slot labels, resulting in improved performance with the ATIS dataset. [PERSON] et al. [4] employed the stack propagation joint model, which allows for the utilization of intent labels as input for the slot filling task. [PERSON] et al. [11] employed joint models in the domain of named entity recognition within the medical field. [PERSON] et al. [12] utilized a unidirectional joint model in downstream tasks of coreference resolution. [PERSON] et al. [13] investigated how to combine specialized models built independently for each task to achieve a complete joint task. They compared the effectiveness of hybrid-based and recurrent convolutional neural network (RCNN) models. The second category involves using both intent and slot labels bidirectionally to enhance the model's performance on these two correlated tasks. For example, [PERSON] et al. [14] designed an iterative mechanism for joint tasks in their proposed network. [PERSON] et al. [15] used stacked blocks to achieve a joint global representation of slot and intent information and released a Chinese dataset. [PERSON] et al. [16] introduced a non-autoregressive model to enhance inference speed. [PERSON] et al. [17] proposed a graph neural network-based model to realize semantic interaction between entities, sentences, and documents. [PERSON] et al. [18] proposed a non-autoregressive model with graph interaction layers. [PERSON] et al. [8] proposed a co-interactive transformer that considers the cross-impact between the two tasks. [PERSON] and [PERSON] [7] proposed a two-stage network, in the first stage of which initial estimation labels for two tasks are generated, and in the second stage, they are used to mutually guide each other. [PERSON] et al. [19] decomposed slot filling into two stages of slot proposal and slot classification to address the significant differences in slot positions for the same intent in different utterances. [PERSON] and [PERSON] [20] constructed a heterogeneous label graph containing two topologies and used the proposed ReLa-Net model to capture label correlations. [PERSON] et al. [21] fully utilized the statistical co-occurrence frequency between intent and slots as prior knowledge to enhance the joint task. [PERSON] et al. [22] encoded domain knowledge using regular expression rules to enhance the model's performance when transferring to a new domain with limited training data. [PERSON] et al. [23] introduced an intent embedding matrix to guide slot filling tasks with intent information and applied it to their released agricultural dataset AGIS. [PERSON] et al. [9] integrated the WordNet knowledge base with a BiLSTM model through an attention mechanism, providing additional semantic information to enhance text comprehension. [PERSON] et al. [24] studied the impact of disfluency detection on downstream tasks of IDSF. [PERSON] et al. [25] further integrated intent inference and slot filling tasks with dialogue state tracking tasks to obtain more accurate dialogue state inference results. [PERSON] et al. [26] introduced a multilingual recurrence-less model for joint tasks. [PERSON] et al. [27] introduced a capsule network structure for Romanian joint tasks. [PERSON] et al. [24] presented the first public IDSF dataset for Vietnamese. [PERSON] et al.