Research On Intent Recognition And Slot Filling Based On Deep Learning

Intention recognition is a sentence classification task that recognizes sentence intent.Slot filling is a sequence labeling problem.The so-called slot is the semantic information of the word.There are many mature theories to study this task,and there have been many mature results in application,such as the chat robot Ali Xiaomi.They can judge your intentions and give answers based on the conversation with you.It plays an important role in organizations like Taobao customer service,saving a lot of manpower and material resources.Of course,there are many shortcomings and even obstacles in the development of intent recognition,such as the problem of data sets.This paper mainly studies various models of intent identification slot filling,compares the pros and cons of the models from multiple angles,and studies the new models.At present,the mainstream research methods for slot filling in intent recognition are based on the research of the joint model.The original two tasks are jointly learned through parameter sharing,and good progress has been made.The research in this article also focuses on the joint model.The following is the main research content of this article:(1)This article applies the TOKEN algorithm to the Chinese data set SMP2019.The TOKEN algorithm is an intent recognition and slot filling algorithm on the English data sets ATIS and SNIPS.ATIS,there are language and task differences between the SNIPS data set and the SMP2019 data set,and the TOKEN algorithm cannot be directly applied to the SMP2019 data set.Therefore,this article refers to the original code and paper of the TOKEN algorithm,rewrites and expands the official baseline of SMP2019,and succes SFully applies the TOKEN algorithm to the SMP2019 data set;(2)At present,the research on intention recognition and slot filling is mostly concentrated on the English data set ATIS and SNIPS.This paper analyzes the results of 7 models on the Chinese data set SMP2019 and discusses the advantages of each model.And shortcomings,and the possibility of fusion between models;(3)This article combines the SF-ID algorithm with the TOKEN algorithm.The characteristic of the TOKEN algorithm is to transform sentence-level classification tasks into word-level classification tasks,so this paper introduces the concepts of word intention vector and word domain vector to represent the sentence intention information and sentence domain information contained in a word.In the SF-ID algorithm,intent classification and domain classification are still regarded as sentence-level classification tasks,and the intent information of the word and the dependence of the domain information and the slot information of the word are not considered,but the sentence-level vector is used for information fusion.This fusion method will generate a lot of redundant information.This paper abandons sentence-level vectors and uses word-level vectors for information fusion,which largely avo IDs the impact of redundant information.In the experiment,the method in this paper has achieved a good improvement in accuracy and f1;(4)Use intent information to enhance domain information based on the TOKEN algorithm.It is found through experiments that the classification effect of intention classification is the best.Therefore,this paper combines the TOKEN algorithm to treat the two sentence-level classification tasks of intent classification and domain classification as multiple word-level classification tasks.The slot filling task is a sequence labeling problem,which can also be understood as a word-level classification task.In this way,the relationship between the intent classification task and the slot classification task can be analogized,and the domain information enhanced based on the intent information can be obtained in a similar way.Compared with the experimental results,the previous methods have a certain degree of improvement in the field classification accuracy and the slot label f1 value;(5)Taking into account the characteristics of the chinese data set,use baidu's ERNIE instead of BERT as the pre-training model.Then add the model of this article,it is found that the experimental results still have a certain improvement.