# Innovation of French Interpretation Teaching Mode in the New Liberal Arts Environment

Authors: Junxiu Zhang
Journal: Journal of Environmental and Public Health
 (2022)

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## Abstract

The status of French as a language has improved to some extent in recent years as a result of the exchange and blending of cultures from various nations. Each language, including French, must undergo a protracted process in order to become more widely spoken. It is essential to develop top-notch French interpreters in order to lower the barriers preventing cross-border communication. The teaching of French interpretation has recently received more attention in many colleges, but the issue of a single teaching method has long persisted. In recent years, a new approach to teaching the liberal arts has emerged. Its main goal is to implement comprehensive interdisciplinary teaching by fusing contemporary information technology with traditional liberal arts instruction. This paper conducted a cutting-edge study on the teaching method of French interpretation against the backdrop of the new liberal arts in order to alter the teaching strategy and increase teaching effectiveness. In this paper, an intelligent interpretation teaching system was constructed using information technology. Through text classification algorithms, it incorporated and optimized teaching resources. According to the experimental findings, the teaching process was optimized, and the teaching efficiency increased by 7.94% when French interpretation was taught against a backdrop of new liberal arts.


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## Body

## 1. Introduction

The increasing frequency of international exchanges has made the gap of French interpreters bigger and bigger, and all sectors of society place their hopes on French graduates from foreign language schools. But it backfired. The knowledge level and language application ability mastered by the graduates were still difficult for interpretation tasks. The reason is that, on the one hand, colleges do not pay enough attention to the teaching of French interpretation, and on the other hand, the teaching mode of interpretation is single and outdated. The arrival of the new liberal arts concept provides new ideas for French interpretation teaching and also brings opportunities for the change of teaching mode.As the main way of international cultural exchange, the importance of interpreting is self-evident, and many scholars have also joined the research ranks of interpreting teaching. Pan et al. designed multimedia interpreting courses to improve the comprehensive ability of non-English majors. The experimental results verified the feasibility of combining multimedia interpreting teaching [1]. Babushkina and Kalugina proposed a software engineering interpretation teaching mode for computer majors in colleges. Practice has proved that this teaching mode can effectively improve students’ enthusiasm for learning [2]. Joly and Iseli-Chan believed that interpreting teaching is essentially the training of skills and techniques. He summed up the four basic skills of consecutive interpreting and brought innovative ideas to the reform of interpreting teaching [3]. Walsh applied chunk theory to interpretation teaching and pointed out the practical significance of interpretation teaching from the perspective of chunk philosophy [4]. Minyoung and Jaehong used relevance theory to analyze the cognitive process and operation process of interpreters in different stages of interpreting and put forward some suggestions for interpreting teaching [5]. Cao proposed a skill-centered teaching method for interpreting. The results of teaching experiments showed that the skills-centered interpretation teaching method has more advantages than the language-centered teaching method [6]. Tian proposed an interpretation teaching module with the help of Podcast. The test results showed that this module complements classroom interpreting teaching and helps to improve the quality of interpreting teaching [7]. The contents of these studies on interpreting teaching are relatively detailed, but they are not related to the new liberal arts.The rapid development of information technology has provided new opportunities for liberal arts education, and there are more and more research on new liberal arts. Xu and Gil discussed the development trend of the library under the new liberal arts. They pointed out that the library should adapt to the new situation of the construction of the new liberal arts. It should meet the new service needs of library users and make active changes in discipline integration, technology application, collaborative support, etc. [8]. Daughtry redefined the new liberal arts teaching concept, analyzed the problems existing in teaching practice, and proposed effective solutions [9]. Weigold et al. analyzed the development of private colleges in the context of the new liberal arts and proposed that private colleges should highlight the advantages of professional characteristics and build a professional liberal arts teaching team [10]. Park studied the training system of tourism management talents in combination with the core significance of the new liberal arts and proposed that the training system of tourism management talents should conform to the general trend of new technology revolution and industrial transformation and should integrate innovative elements in practical teaching [11]. Han discussed the new liberal arts teaching in the context of the information age and pointed out that relevant colleges should innovate teaching models and create intelligent learning classrooms to improve teaching efficiency [12]. Kim discussed the current situation of computer basic education in the University of Arts and Sciences under the background of the new liberal arts. Through the reconstruction and improvement of the diversified teaching resource framework, the learning initiative of liberal arts students has been enhanced, and the learning efficiency of students has been improved [13]. Rizzi explored a new liberal arts teaching approach to Christianity. Practice has proved that the level of academic performance is positively related to study skills and teaching methods [14]. The above research work on the new liberal arts is relatively specific, but there is no research on French interpretation teaching in the context of the new liberal arts.Relevant colleges must innovate their interpreting teaching methods and change their teaching philosophies in order to develop more French interpreting talent for society. The new liberal arts concept proposal has given relevant colleges new opportunities. Applying the new liberal arts idea to the instruction of French interpretation is an attempt and a transformation. This essay examined the French interpretation teaching method within the framework of the new liberal arts. By implementing an intelligent teaching system and building a new teaching classroom, it increased the effectiveness of interpretation teaching.


## 2. New Liberal Arts Teaching Philosophy

The reorganization of various professional courses in the discipline around the traditional liberal arts to create the intersection of the arts and sciences is known as the “new liberal arts.” To put it another way, it is the incorporation of contemporary information technology [15, 16] into subjects like philosophy, literature, language, and other similar courses in order to give students a thorough interdisciplinary learning experience for the benefit of knowledge expansion and creative thinking. The historical purpose of traditional liberal arts has been achieved. It has created circumstances and laid the groundwork for discussions about and the implementation of the new liberal arts system. The traditional liberal arts and the contemporary liberal arts are not interchangeable. It is also not an adversarial relationship. The new liberal arts do not negate or eliminate the traditional liberal arts; rather, they critique, revise, incorporate, enhance, and promote the traditional liberal arts. It is hoped that disciplinary and professional barriers will be broken through the intersection and integration of the internal disciplines of the liberal arts as well as the intersection and integration of the liberal arts with science, engineering, agriculture, and medicine. To study, comprehend, and solve complex problems in the discipline itself, with people, and in society, with a broader academic perspective, a broader awareness of problems, and a deeper academic accumulation in order to cultivate new talents that better meet the needs of modern society, as shown in Figure 1, there are five components to the requirements for new talents under the new liberal arts concept: practical operation ability, academic research ability, communication management ability, technology application ability, and data analysis ability.Figure 1
Requirements for new talents under the new liberal arts concept.The new liberal arts is an innovative development in the field of liberal arts education and a higher education development strategy that is in line with the new era. Figure2 shows the three dimensions and four systems of teaching reform under the new liberal arts concept. The three dimensions include strengthening value guidance, creating digital humanities, and demonstrating the quality of liberal arts. The four systems include theoretical system, teaching system, discipline system, and evaluation system. The new liberal arts pursues the transition from “discipline management” to breaking down the barriers of disciplines and realizes “discipline integration” of interdisciplinary in terms of professional content and teaching methods.Figure 2
Three dimensions and four systems of teaching reform under the new liberal arts concept.


## 3. French Interpretation Teaching Mode Combined with the New Liberal Arts Concept

### 3.1. Process of French Interpretation

Whether it is French or other languages, their interpretation process is basically the same. Like other things, the interpreting work also has its inherent laws and operating processes. Only by understanding and mastering the laws can interpreters achieve good interpreting results in the interpreting process. The specific process of interpretation can be divided into five links: input, listening, understanding, memory, and expression. The five links are closely connected and inseparable from each other, and some links are almost synchronized, as shown in Figure3.Figure 3
The process of french interpretation.Input is the input of language information [17]. In other words, it is the content that needs to be interpreted in French. Except for sight translation, which receives information through sight, in most cases, the interpreter receives language information from the speaker through hearing. Therefore, hearing clearly is the first pass of interpretation. If it is not fully understood or misunderstood, it is impossible to accurately and completely convey the original intention of the speaker and even cause misunderstanding, leading to the failure of communication between the two parties or more serious consequences. Understanding is the process in which interpreters make correct judgments after analyzing, interpreting, and synthesizing the received language information. Memory means that the interpreter temporarily stores the information in the memory in the process of processing the received language information so as to prevent the phenomenon of omission and mistranslation during conversion and expression. Expression is the process in which the interpreter expresses the original intention of the speaker in French on the basis of fully understanding the original language. This step is the most important step, because the ultimate purpose of hearing, understanding, and remembering is expression.


### 3.2. Intelligent Teaching System for French Interpretation under the New Liberal Arts

The new liberal arts concept emphasizes the application of digital means such as information technology in teaching. This paper combines information technology to build an intelligent French interpretation teaching system, as shown in Figure4. The specific process of the system is as follows: first, students watch teaching videos and contact French interpreting before class, then form a flipped classroom (flipping learning and teaching), and finally conduct interpreting training and share experience.Figure 4
French interpretation intelligent teaching system.The flipped classroom is the core of the intelligent teaching system for French interpretation. It takes “students’ learning” as the core and flips “teaching” and “learning.” Before the class, the teacher introduces the relevant interpreting course materials to the students in various forms, such as microclasses. Students should have a general understanding of these materials before the class because the time in the class should be used for teacher-student interaction and answering questions. Under the support of information technology, the flipped classroom has been introduced into the teaching of interpreting. Students can obtain information, perceive information, and transmit information through more abundant channels. They can complete the understanding and memory of interpreting skills before class and can complete the understanding of relevant topic information. In classroom teaching, students can also strengthen the cultivation of interpretation skills through teacher-student interaction, student-student interaction, group interaction, and human-computer interaction. After class, students can conduct independent learning through platforms such as online open classes and interpretation corpus. This intelligent learning model breaks through the limitations of traditional classroom space and time, making ubiquitous learning possible. Through the flipped classroom, teachers can also provide unique curriculum resources for different learners according to the characteristics of students. They can establish a network communication platform to facilitate students to communicate before and after class and obtain learning feedback information, and at the same time improve the quality of “teaching” and “learning.”


### 3.3. Course Design of French Interpretation Based on Collaborative Teaching

Collaborative teaching is a branch of the new liberal arts teaching concept. Combining with information technology, this paper designed a French interpretation course based on collaborative teaching. The course is divided into two parts, as shown in Figure5. Among them, the comprehensive course of knowledge explanation is divided into four modules: basic interpretation, listening and distinguishing, Chinese-French consecutive interpretation, and sight translation; the skill-based course is divided into two modules: interpretation workshop and accompanying liaison interpretation. The specific idea of this course is to use the French interpretation knowledge as the basis, then match it with relevant interpretation drills, and finally use the live broadcast function in modern information technology to interact and evaluate.Figure 5
French interpretation course based on collaborative teaching.


### 3.4. Evaluation System of French Interpretation Teaching

Combined with the background of the new liberal arts, this paper proposed a multimodal evaluation method that runs through the entire interpretation teaching process, which is divided into “teacher-student evaluation,” “student-student evaluation,” and “self-assessment.”Teacher-student evaluation: Teachers evaluate students as a whole by observing students’ learning attitudes, understanding the mastery of interpreting learning, recording the performance of classroom activities, and analyzing the quality of interpreting recordings.Student-to-student evaluation: Students’ mutual evaluation under the guidance of teachers is a very effective incentive mechanism. According to the performance of preclass microclass learning exchanges, topic discussions, classroom demonstrations, recording analysis, actual combat simulations, etc., students’ evaluation of each other helps them to learn from each other and improve themselves.Self-assessment: Any effective learning is inseparable from the learner’s self-monitoring, self-analysis, self-reflection, and self-regulation. During the interpreting exercise, students can use mobile phone software to convert the speech into text to quickly correct errors. Or they can use the multimodal corpus to upload the interpretation recordings, then compare them with the reference translations. Finally, they can record, analyze, organize, and correct them in the process of self-evaluation.In general, the ultimate goal of the multimodal assessment method is to improve the learning efficiency of students and cultivate qualified French interpreters for society.


## 3.1. Process of French Interpretation

Whether it is French or other languages, their interpretation process is basically the same. Like other things, the interpreting work also has its inherent laws and operating processes. Only by understanding and mastering the laws can interpreters achieve good interpreting results in the interpreting process. The specific process of interpretation can be divided into five links: input, listening, understanding, memory, and expression. The five links are closely connected and inseparable from each other, and some links are almost synchronized, as shown in Figure3.Figure 3
The process of french interpretation.Input is the input of language information [17]. In other words, it is the content that needs to be interpreted in French. Except for sight translation, which receives information through sight, in most cases, the interpreter receives language information from the speaker through hearing. Therefore, hearing clearly is the first pass of interpretation. If it is not fully understood or misunderstood, it is impossible to accurately and completely convey the original intention of the speaker and even cause misunderstanding, leading to the failure of communication between the two parties or more serious consequences. Understanding is the process in which interpreters make correct judgments after analyzing, interpreting, and synthesizing the received language information. Memory means that the interpreter temporarily stores the information in the memory in the process of processing the received language information so as to prevent the phenomenon of omission and mistranslation during conversion and expression. Expression is the process in which the interpreter expresses the original intention of the speaker in French on the basis of fully understanding the original language. This step is the most important step, because the ultimate purpose of hearing, understanding, and remembering is expression.


## 3.2. Intelligent Teaching System for French Interpretation under the New Liberal Arts

The new liberal arts concept emphasizes the application of digital means such as information technology in teaching. This paper combines information technology to build an intelligent French interpretation teaching system, as shown in Figure4. The specific process of the system is as follows: first, students watch teaching videos and contact French interpreting before class, then form a flipped classroom (flipping learning and teaching), and finally conduct interpreting training and share experience.Figure 4
French interpretation intelligent teaching system.The flipped classroom is the core of the intelligent teaching system for French interpretation. It takes “students’ learning” as the core and flips “teaching” and “learning.” Before the class, the teacher introduces the relevant interpreting course materials to the students in various forms, such as microclasses. Students should have a general understanding of these materials before the class because the time in the class should be used for teacher-student interaction and answering questions. Under the support of information technology, the flipped classroom has been introduced into the teaching of interpreting. Students can obtain information, perceive information, and transmit information through more abundant channels. They can complete the understanding and memory of interpreting skills before class and can complete the understanding of relevant topic information. In classroom teaching, students can also strengthen the cultivation of interpretation skills through teacher-student interaction, student-student interaction, group interaction, and human-computer interaction. After class, students can conduct independent learning through platforms such as online open classes and interpretation corpus. This intelligent learning model breaks through the limitations of traditional classroom space and time, making ubiquitous learning possible. Through the flipped classroom, teachers can also provide unique curriculum resources for different learners according to the characteristics of students. They can establish a network communication platform to facilitate students to communicate before and after class and obtain learning feedback information, and at the same time improve the quality of “teaching” and “learning.”


## 3.3. Course Design of French Interpretation Based on Collaborative Teaching

Collaborative teaching is a branch of the new liberal arts teaching concept. Combining with information technology, this paper designed a French interpretation course based on collaborative teaching. The course is divided into two parts, as shown in Figure5. Among them, the comprehensive course of knowledge explanation is divided into four modules: basic interpretation, listening and distinguishing, Chinese-French consecutive interpretation, and sight translation; the skill-based course is divided into two modules: interpretation workshop and accompanying liaison interpretation. The specific idea of this course is to use the French interpretation knowledge as the basis, then match it with relevant interpretation drills, and finally use the live broadcast function in modern information technology to interact and evaluate.Figure 5
French interpretation course based on collaborative teaching.


## 3.4. Evaluation System of French Interpretation Teaching

Combined with the background of the new liberal arts, this paper proposed a multimodal evaluation method that runs through the entire interpretation teaching process, which is divided into “teacher-student evaluation,” “student-student evaluation,” and “self-assessment.”Teacher-student evaluation: Teachers evaluate students as a whole by observing students’ learning attitudes, understanding the mastery of interpreting learning, recording the performance of classroom activities, and analyzing the quality of interpreting recordings.Student-to-student evaluation: Students’ mutual evaluation under the guidance of teachers is a very effective incentive mechanism. According to the performance of preclass microclass learning exchanges, topic discussions, classroom demonstrations, recording analysis, actual combat simulations, etc., students’ evaluation of each other helps them to learn from each other and improve themselves.Self-assessment: Any effective learning is inseparable from the learner’s self-monitoring, self-analysis, self-reflection, and self-regulation. During the interpreting exercise, students can use mobile phone software to convert the speech into text to quickly correct errors. Or they can use the multimodal corpus to upload the interpretation recordings, then compare them with the reference translations. Finally, they can record, analyze, organize, and correct them in the process of self-evaluation.In general, the ultimate goal of the multimodal assessment method is to improve the learning efficiency of students and cultivate qualified French interpreters for society.


## 4. Application of Text Classification Algorithms in Interpretation Teaching

In order to better demonstrate the practical effect of the French interpreting teaching system under the new liberal arts, this paper applied a variety of text classification algorithms to interpreting teaching, aiming to provide more accurate and targeted interpreting learning resources for students.


### 4.1. Information Gain IG

Information gain is a statistic that describes the ability of an attribute to distinguish data samples. When knowing whether a feature word is in the document, the information gain value can predict the category of the obtained information bits [18]. The specific calculation method is as follows:(1)IZ,z=GZ−GZ|z=−∑c∈CprZd=clogprZd=c+∑c∈CprZd=c,z=0logprZd=c|z=0+∑c∈CprZd=c,z=1logprZd=c|z=1.In the formula,GZ represents the average information entropy of all categories, GZ|z represents the average conditional information entropy of all categories when it is known whether a feature word z appears in the document. prZd=c,z=0 represents the probability that documents without feature word z appear in category c, prZd=c,z=1 represents the probability that the document containing the feature word z appears in the category c.prZd=c represents the probability that the document d belongs to the category c.


### 4.2. Mutual Information

First introduce the following symbols (in these formulas,a and b represent feature words and categories, respectively):A: The number of training documents of categoryb and including feature a, denoted as DFa∧b.B: The number of training documents that do not belong to categoryb (or belong to category a) and contain feature a, denoted as DFa∧b¯.C: The number of training documents that belong to categoryb and do not contain feature a, denoted as DFa¯∧b.D: The number of training documents that do not belong to categoryb and do not contain feature a, denoted as DFa¯∧b¯.M: The number of training documents belonging to categoryb.N: The total number of training documents.From the above it follows thatA + C = M and A + B + C + D=N.Mutual information represents the association information between categories and feature words. That is, the mutual information between categoryb and feature word a can be defined as follows:(2)Ia,b=logpra∧bpraprb.In the formula,pr is the probability operation symbol. Assuming that pra∧b is the probability of a and b appearing at the same time, then(3)Ia,b=logA/NA+B/N∗A+C/N=A×NA+CA+B.In formula (3), a and b are independent of each other, so their mutual information is 0. Considering all the possibilities of feature names, the document category can take the maximum value or statistical expectation:(4)Iavga=∑c∈CprbIa,b,Imaxa=maxc∈CIa,b.


### 4.3.x2 Statistics (CHI)

x2 statistic is used to measure the statistical correlation between feature a and category b, which can be expressed as follows:(5)CHIa,b=N∗pa,b−pa∗pb2pa∗pb+pa,b¯−pa∗pb¯2pa∗pb¯++N∗pa¯,b−pa¯∗pb2pa¯∗pb+pa¯,b¯−pa¯∗pb¯2pa¯∗pb¯+,then(6)CHIa,b=N∗pa,b∗pa¯,b¯−pa,b¯∗pa¯,b2pa∗pa¯∗pb∗pb¯.Substituting the frequency of each event for its corresponding probability, thex2 statistic CHIa,b can be approximated by the following:(7)CHIa,b≈N∗A∗D−B∗C2A+C∗B+D∗A+B∗C+D.ConsideringN, A + C, B + D are all constants, formula (7) can be simplified as follows:(8)CHIa,b≈A∗D−B∗C2A+B∗C+D.In formula (8), the stronger the correlation between feature a and category b, the greater the value of CHIa,b. When feature a and category b are independent of each other, CHI (a, b) = 0, feature a does not contain any discriminating information related to category b.


### 4.4. Weight of Evidence Text

The formula for calculating the right of text evidence is as follows:(9)Weight of Evid TxtW,Dj=QW×logQDj|W1−QDjQDj1−QDj|W.In the formula,QW represents the frequency of feature W, QDj|W represents the frequency of text containing feature W belonging to category Dj, QDj represents the frequency of category Dj. The weight of textual evidence compares the difference between the probability of a class occurrence and the conditional probability of the class occurrence, given the characteristics. If the entry and the category are strongly related, and the probability of the corresponding category appears is small. It means that the entry has a great influence on the classification, the calculated function value is large, and it can be selected as the feature item. If the probability of occurrence of the category is high, and the entry is weakly related to the category. It means that the entry has little influence on the classification, the calculated function value is small, and it will not be selected as a feature item [19].


### 4.5. Naive Bayes

Naive Bayesian text classification methods include Bayesian statistical models and Bayesian classification models. The specific process of the Bayesian statistical model is to use prior distribution and posterior distribution. Assuming that eventsA1,A2,A3,…,An constitute a complete set of incompatible events p, the Bayesian formula is as follows:(10)pAi|BjpBj|AipAi∑i=1kpBj|AipAi,i=1,2,…,n,j=1,2,…,k.In formula (10), the prior information is given in the form of a probability distribution of pAi,i=1,2,3,…,n, that is, the prior distribution. As a result of the occurrence of event Bj, new information can be provided on the probability of occurrence of A1,A2,A3,…,An. From this information and the prior distribution, the posterior distribution pAi|Bj,i=1,2,3,…,k can be derived.The main idea of the Bayesian classification model is to assume that the influence of a factor in a given category is independent of other factors, which can also be called the independent factor assumption. Supposing the training sample set is divided intok categories, denoted as O=O1,O2,…,Ok. The prior probability of each category Oi is: pOi,i=1,2,…,k. For the sample d, its conditional probability of belonging to class Oi is pOi|d. According to the Bayesian classification idea, the posterior probability of document category Oi is as follows:(11)pOi|d=pd|OipOipd.Sincepd is constant for all documents, formula (11) can be simplified to the following:(12)pOi|d=∞pd|OipOi.In order to avoidpOi being equal to zero, the Laplace probability estimate [20] is used:(13)pOi=1+DOiO+Do.In formula (13), O is the number of categories in the training set, DOi is the number of documents belonging to Oi categories in the training set, and DO is the total number of documents contained in the training set. When special documents appear, since the prior probabilities of document categories are equal, formula (13) can be simplified to the following:(14)pOi|d=∞pd|Oi.The naive Bayes classification method categorizes unknown samples according to the following:(15)pOi|d=maxpd|OjpOj,j=1,2,…,k.Among themd=t1,…,tj,…,tm, m is the number of feature words d of d, tj is the j-th feature word. According to the principle of feature independence, the following can be got:(16)pd|Oi=pt1,t2,…,tm|Oi∏j=1mptj|Oi.In the formula,ptj|Oi represents the probability that word tj occurs in the document of category Oi during the classification process, so formula (16) can be transformed into the following:(17)pOi|d=pOi∞∏j=1mptj|Oi.In order to avoidptj|Oi=0 in formula (17), Laplace probability estimation can be used, and the specific forms are divided into two types: document type and word frequency type.Document type: The frequency of the word in the document is not considered, it only considers whether the word appears in the document. 0 means not appearing, 1 means appearing:(18)ptj|Oi=1+Ndoctj|Oi2+Do.In formula (18), Ndoctj|Oi is the number of texts in which feature tj appears in type Oi texts.Word frequency type: Considering the frequency of words in the document, the specific calculation method is as follows:(19)ptj|Oi=1+TFtj,OiV+∑k=1vTFtk,Oi.In the formula,V represents the total number of words in the feature vocabulary, and TFtj,Oi represents the sum of the frequency of word tj in all documents of Oi categories.


## 4.1. Information Gain IG

Information gain is a statistic that describes the ability of an attribute to distinguish data samples. When knowing whether a feature word is in the document, the information gain value can predict the category of the obtained information bits [18]. The specific calculation method is as follows:(1)IZ,z=GZ−GZ|z=−∑c∈CprZd=clogprZd=c+∑c∈CprZd=c,z=0logprZd=c|z=0+∑c∈CprZd=c,z=1logprZd=c|z=1.In the formula,GZ represents the average information entropy of all categories, GZ|z represents the average conditional information entropy of all categories when it is known whether a feature word z appears in the document. prZd=c,z=0 represents the probability that documents without feature word z appear in category c, prZd=c,z=1 represents the probability that the document containing the feature word z appears in the category c.prZd=c represents the probability that the document d belongs to the category c.


## 4.2. Mutual Information

First introduce the following symbols (in these formulas,a and b represent feature words and categories, respectively):A: The number of training documents of categoryb and including feature a, denoted as DFa∧b.B: The number of training documents that do not belong to categoryb (or belong to category a) and contain feature a, denoted as DFa∧b¯.C: The number of training documents that belong to categoryb and do not contain feature a, denoted as DFa¯∧b.D: The number of training documents that do not belong to categoryb and do not contain feature a, denoted as DFa¯∧b¯.M: The number of training documents belonging to categoryb.N: The total number of training documents.From the above it follows thatA + C = M and A + B + C + D=N.Mutual information represents the association information between categories and feature words. That is, the mutual information between categoryb and feature word a can be defined as follows:(2)Ia,b=logpra∧bpraprb.In the formula,pr is the probability operation symbol. Assuming that pra∧b is the probability of a and b appearing at the same time, then(3)Ia,b=logA/NA+B/N∗A+C/N=A×NA+CA+B.In formula (3), a and b are independent of each other, so their mutual information is 0. Considering all the possibilities of feature names, the document category can take the maximum value or statistical expectation:(4)Iavga=∑c∈CprbIa,b,Imaxa=maxc∈CIa,b.


## 4.3.x2 Statistics (CHI)

x2 statistic is used to measure the statistical correlation between feature a and category b, which can be expressed as follows:(5)CHIa,b=N∗pa,b−pa∗pb2pa∗pb+pa,b¯−pa∗pb¯2pa∗pb¯++N∗pa¯,b−pa¯∗pb2pa¯∗pb+pa¯,b¯−pa¯∗pb¯2pa¯∗pb¯+,then(6)CHIa,b=N∗pa,b∗pa¯,b¯−pa,b¯∗pa¯,b2pa∗pa¯∗pb∗pb¯.Substituting the frequency of each event for its corresponding probability, thex2 statistic CHIa,b can be approximated by the following:(7)CHIa,b≈N∗A∗D−B∗C2A+C∗B+D∗A+B∗C+D.ConsideringN, A + C, B + D are all constants, formula (7) can be simplified as follows:(8)CHIa,b≈A∗D−B∗C2A+B∗C+D.In formula (8), the stronger the correlation between feature a and category b, the greater the value of CHIa,b. When feature a and category b are independent of each other, CHI (a, b) = 0, feature a does not contain any discriminating information related to category b.


## 4.4. Weight of Evidence Text

The formula for calculating the right of text evidence is as follows:(9)Weight of Evid TxtW,Dj=QW×logQDj|W1−QDjQDj1−QDj|W.In the formula,QW represents the frequency of feature W, QDj|W represents the frequency of text containing feature W belonging to category Dj, QDj represents the frequency of category Dj. The weight of textual evidence compares the difference between the probability of a class occurrence and the conditional probability of the class occurrence, given the characteristics. If the entry and the category are strongly related, and the probability of the corresponding category appears is small. It means that the entry has a great influence on the classification, the calculated function value is large, and it can be selected as the feature item. If the probability of occurrence of the category is high, and the entry is weakly related to the category. It means that the entry has little influence on the classification, the calculated function value is small, and it will not be selected as a feature item [19].


## 4.5. Naive Bayes

Naive Bayesian text classification methods include Bayesian statistical models and Bayesian classification models. The specific process of the Bayesian statistical model is to use prior distribution and posterior distribution. Assuming that eventsA1,A2,A3,…,An constitute a complete set of incompatible events p, the Bayesian formula is as follows:(10)pAi|BjpBj|AipAi∑i=1kpBj|AipAi,i=1,2,…,n,j=1,2,…,k.In formula (10), the prior information is given in the form of a probability distribution of pAi,i=1,2,3,…,n, that is, the prior distribution. As a result of the occurrence of event Bj, new information can be provided on the probability of occurrence of A1,A2,A3,…,An. From this information and the prior distribution, the posterior distribution pAi|Bj,i=1,2,3,…,k can be derived.The main idea of the Bayesian classification model is to assume that the influence of a factor in a given category is independent of other factors, which can also be called the independent factor assumption. Supposing the training sample set is divided intok categories, denoted as O=O1,O2,…,Ok. The prior probability of each category Oi is: pOi,i=1,2,…,k. For the sample d, its conditional probability of belonging to class Oi is pOi|d. According to the Bayesian classification idea, the posterior probability of document category Oi is as follows:(11)pOi|d=pd|OipOipd.Sincepd is constant for all documents, formula (11) can be simplified to the following:(12)pOi|d=∞pd|OipOi.In order to avoidpOi being equal to zero, the Laplace probability estimate [20] is used:(13)pOi=1+DOiO+Do.In formula (13), O is the number of categories in the training set, DOi is the number of documents belonging to Oi categories in the training set, and DO is the total number of documents contained in the training set. When special documents appear, since the prior probabilities of document categories are equal, formula (13) can be simplified to the following:(14)pOi|d=∞pd|Oi.The naive Bayes classification method categorizes unknown samples according to the following:(15)pOi|d=maxpd|OjpOj,j=1,2,…,k.Among themd=t1,…,tj,…,tm, m is the number of feature words d of d, tj is the j-th feature word. According to the principle of feature independence, the following can be got:(16)pd|Oi=pt1,t2,…,tm|Oi∏j=1mptj|Oi.In the formula,ptj|Oi represents the probability that word tj occurs in the document of category Oi during the classification process, so formula (16) can be transformed into the following:(17)pOi|d=pOi∞∏j=1mptj|Oi.In order to avoidptj|Oi=0 in formula (17), Laplace probability estimation can be used, and the specific forms are divided into two types: document type and word frequency type.Document type: The frequency of the word in the document is not considered, it only considers whether the word appears in the document. 0 means not appearing, 1 means appearing:(18)ptj|Oi=1+Ndoctj|Oi2+Do.In formula (18), Ndoctj|Oi is the number of texts in which feature tj appears in type Oi texts.Word frequency type: Considering the frequency of words in the document, the specific calculation method is as follows:(19)ptj|Oi=1+TFtj,OiV+∑k=1vTFtk,Oi.In the formula,V represents the total number of words in the feature vocabulary, and TFtj,Oi represents the sum of the frequency of word tj in all documents of Oi categories.


## 5. Experimental Results of the French Interpretation Teaching Mode Combined with the New Liberal Arts Concept

The new liberal arts is a new teaching concept put forward in recent years. It is not mature enough at present, and many people have mixed praise and criticism for its application in teaching. In order to have a clearer understanding of the popularity of the new liberal arts in colleges, this paper investigated the understanding of the new liberal arts among teachers and students in a foreign language college and the help of the new liberal arts concept in the teaching of French interpretation. The specific number is 500 people in total including teachers and students. Among them, the degree of understanding is divided into very understanding, basic understanding, heard of, and no knowledge; the help status is divided into very helpful, helpful, maybe helpful, and not helpful. The specific situation is shown in Figure6.Figure 6
Teachers and students’ understanding of the new liberal arts and the help of the new liberal arts concept in french interpreting teaching.From the bar chart in Figure6, it can be seen that the teachers’ understanding of the new liberal arts is still relatively high. Most of the students are concentrated on the part with very good and basic understanding, and very few do not understand. However, the students’ understanding of the new liberal arts is relatively low. Many people are at the level of half-understanding. In terms of help status, the teachers’ opinions are also very consistent. Basically, they all think that the addition of the new liberal arts concept is helpful to the teaching of French interpreting. Due to the low level of understanding of the students, most of them naturally think that there may be a help.In order to verify the implementation effect of the intelligent teaching mode of French interpreting under the new liberal arts, this paper investigated the degree of satisfaction of students in a foreign language college with the mode. The specific number is 300, and the degree of satisfaction is presented in the form of scoring. Below 60 points are dissatisfied, 60–70 points are not satisfied, 70–80 points are relatively satisfied, 80–90 points are satisfied, and 90–100 points are very satisfied. The survey results are shown in Table1.Table 1
Students’ satisfaction with the intelligent teaching mode of French interpretation.
FractionQuantityPercentage (%)Below 6072.3360–70113.6770–80196.3380–90451590–10021872.67In order to compare students’ satisfaction with the French interpreting teaching model more intuitively, this paper plotted the survey results as a bar chart in Figure7.Figure 7
Students’ satisfaction with the intelligent teaching model of french interpretation.From Table1 and the bar chart in Figure 7, it can be clearly seen that most students are very satisfied with the intelligent teaching mode of French interpretation under the new liberal arts. A small number of students are relatively satisfied, and very few are not satisfied, which confirms the implementation of this model is quite successful.In the method part, this paper mentioned collaborative teaching and flipped classrooms. In order to test the practical effect of these two methods in French interpreting teaching, the learning enthusiasm and classroom participation rate of students in ordinary teaching and ordinary classrooms are compared with the students in collaborative teaching and flipped classrooms. The maximum value of the enthusiasm and participation rate was set at 100, and the specific time was the class time within eight weeks. The comparison results are shown in Figures8 and 9.Figure 8
Comparison of learning enthusiasm and classroom participation rate under ordinary teaching and learning enthusiasm and classroom participation rate under collaborative teaching.Figure 9
Comparison of learning enthusiasm and classroom participation rate in the ordinary classroom and flipped classroom.Figure8 shows the comparison of the learning enthusiasm and classroom participation rate under ordinary teaching and the learning enthusiasm and classroom participation rate under collaborative teaching. It can be seen from the line chart that the learning enthusiasm and classroom participation rate under the ordinary teaching mode are much lower than those of the collaborative teaching mode, and the fluctuation trend is obvious. At the same time, the learning enthusiasm and classroom participation rate under the collaborative teaching mode have been kept at a relatively high level, and the upward trend is obvious.Figure9 shows the comparison of the learning enthusiasm and classroom participation rate in the ordinary classroom and the learning enthusiasm and classroom participation rate under the flipped classroom. It can be seen that there are fluctuations in the learning enthusiasm and classroom participation rate in the two classrooms, but the fluctuation trends are quite different. The flipped classroom is obviously much smaller than the ordinary classroom. In general, the learning enthusiasm and classroom participation rate in the flipped classroom are on the rise, and the learning enthusiasm and classroom participation rate in the ordinary classroom are in a declining state.The ultimate purpose of innovating and improving the French interpretation teaching mode is to improve the teaching efficiency, so as to cultivate more interpretation talents. Figure10 shows the comparison of teaching efficiency between the traditional French interpreting teaching model and the French interpreting teaching model under the new liberal arts. The full score for efficiency is 10 points, and the sample is the teaching efficiency of a foreign language college for one year.Figure 10
Comparison of teaching efficiency between the traditional french interpretation teaching mode and the french interpretation teaching mode under the new liberal arts.It can be clearly seen from the graph that the teaching efficiency of each time period is different under the two teaching modes, and there will be a downward trend. This phenomenon is normal because the learning environment and learning content arranged by colleges and universities will be different in each time period, and the status of teachers and students will also fluctuate. Overall, the teaching efficiency based on the new liberal arts teaching model is relatively stable and is 7.94% higher than the traditional teaching model.


## 6. Conclusion

French is one of the most widely spoken languages in the world, and its importance in cross-cultural communication should not be understated. The shortage of French interpreting skills has been growing as international cultural and economic exchanges continue to deepen. Foreign language colleges, which serve as the primary hub for the transfer of interpreting talent, are also coming under increasing pressure to improve their interpreting curricula. Relevant colleges should modify their teaching strategies for interpretation in light of the new liberal arts. In order to lay a strong foundation for the future development of French interpretation teaching, they should innovate the teaching approach in conjunction with the new era and new technologies and optimise the teaching procedure.



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