Research on Spoken Language Understanding Based on Deep Learning

Abstract

Aiming at solving the problem that the recognition effect of rare slot values in spoken language is poor, which affects the accuracy of oral understanding task, a spoken language understanding method is designed based on deep learning. The local features of semantic text are extracted and classified to make the classification results match the dialogue task. An intention recognition algorithm is designed for the classification results. Each datum has a corresponding intention label to complete the task of semantic slot filling. The attention mechanism is applied to the recognition of rare slot value information, the weight of hidden state and corresponding slot characteristics are obtained, and the updated slot value is used to represent the tracking state. An auxiliary gate unit is constructed between the upper and lower slots of historical dialogue, and the word vector is trained based on deep learning to complete the task of spoken language understanding. The simulation results show that the proposed method can realize multiple rounds of man-machine spoken language. Compared with the spoken language understanding methods based on cyclic network, context information, and label decomposition, it has higher accuracy and F1 value and has higher practical application value.

State of the Art in Intent Detection and Slot Filling for Question Answering System: A Systematic Literature Review

“…On top of that, there are also published papers with comprehensive reviews [50-51, 53, 55]. The aim of [50][51] is to conduct a literature review about Intents, Intention Mining, and IC and focuses on the review of algorithms, models, and tools that have been implemented in Intention Mining. A review paper on the ID methods in the human-machine dialogue system seeks to advance the study of multi-intent detection methods based on Recurrent Neural Networks [52] and deep neural networks [53].…”

“…Therefore, one of the aims of this paper is to focus on conducting a systematic literature review about ID and IC with slot filling in which we are not focusing on the implementation, but we are going deeper into the investigation and discussion of the framework, methodology, and techniques or algorithms that can be implemented in ID and IC with slot filling. Furthermore, what we are reviewing differs from [51,53,55], which reviews the topic of intention mining, ID methods in the human-machine dialogue system, and methods of two tasks from the independent model to the joint model, respectively. This paper is divided as follows: Section II presents the conducted systematic review methodology that consists of the definition of research questions, search phases, inclusion and exclusion criteria, and paper eligibility screening.…”

Intent Classification and Slot Filling Model for In-Vehicle Services in Korean

“…Moreover, they are built with the assumption that the user's request is limited to a small number of intents, such as "find", or "book" compared to our expectation from in-vehicle AI speakers. To avoid the limited coverage and the small number of intents of in-vehicle AI speaker services in English, the studies that consider the users who are under the condition of in-vehicle appear as well [15][16][17].…”