# Semantically Guided Enhanced Fusion for Intent Detection and Slot Filling
## 1 Introduction  
Intent detection and slot filling (IDSF) are quintessential components in spoken language understanding (SLU) systems and are instrumental in decoding user inquiries [1]. These tasks are pivotal for natural language understanding (NLU) in man-machine conversational interfaces. Intent detection is tasked with discerning the user's objective from their input, while slot filling involves identifying specific entities that provide detailed context for the intent. For instance, in command \"Aircraft No. 3 takeoff to intercept enemy aircraft in the southwestern\", the task of intent detection is to identify its intent label of \"interception\" from a predefined label set. Concurrently, the task of slot filling is to generate slot labels for each word in command, such as \"B-our attacking unit\", \"I-our attacking unit\", \"E-our attacking unit\", \"O\", and \"S-direction\". Since errors of IDSF will propagate to downstream tasks such as dialogue state tracking, and ultimately negatively affect the user experience of SLU systems, it is worth pursuing solutions with higher accuracy for IDSF. In recent years, due to the significant correlation between intent and slot information, numerous studies have made efforts to leverage this connection. Some research exploits intent information as supplementary data to enhance slot filling [2, 3, 4], while other works employ joint models to extract slots and intents simultaneously [5, 6, 7, 8]. For instance, in the former type of methods, [PERSON] et al. [2] achieved superior semantic framing results by leveraging the relationship between slot-gate learning intentions and slot attention vectors. [PERSON] et al. [3] utilized intention-enhanced embeddings, obtained via a neural network with a self-attentive mechanism, as an entry point for marking slot labels. [PERSON] et al. [4] captured intent semantic knowledge by directly employing intent information as input for slot filling. In the latter approach, for instance, [PERSON] et al. [5] modeled the strongcorrelation between slots and intents by introducing an intent-slot graph interaction layer. [PERSON] et al. [6] incorporated attention to intent and slot information into alignment-based recurrent neural network (RNN) models, furnishing additional data for intent classification and slot label prediction. [PERSON] et al. [7] effectively represented the relationship between semantic nodes and labeled nodes by initializing the labels of two tasks and constructing two heterogeneous graph attention networks over them. [PERSON] et al. [8] considered cross-influence by establishing a two-way connection between the two related tasks, enabling slots and intents to focus on mutual information. However, the aforementioned studies have either focused solely on a unidirectional flow of information from intent to slot, or concentrated on the interplay between these two tasks, thereby overlooking the semantics of the text. Inspired by [PERSON] et al. 's [9] introducing knowledge base as complementary for IDSF, we argue that the semantics of text should be adequately utilized to achieve better performance. Specifically, we introduce a semantic guidance (SG) framework for IDSF tasks. To effectively extract slot and intent information, a feature extractor is employed to distill slot and intent information from text into word embedding features. Subsequently, a feature fusion module with a dynamic gating strategy is devised to amalgamate slot information, intent information, and text information. The slot and intent information are harnessed to guide the textual representation, enhancing the accuracy of IDSF. To further validate our proposed method, experiments were conducted on two public datasets. By concurrently investigating inter-modal and intra-modal relationships, it is concluded that the method of utilizing slot information and intention information to guide textual representations significantly augments the performance of the model.