Classification of Conversational Sentences Using an Ensemble Pre-Trained Language Model with the Fine-Tuned Parameter

Sentence classification is the process of categorizing a sentence based on the context of the sentence.Sentence categorization requires more semantic highlights than other tasks,such as dependence parsing,which requires more syntactic elements.Most existing strategies focus on the general semantics of a conversation without involving the context of the sentence,recognizing the progress and comparing impacts.An ensemble pre-trained language model was taken up here to classify the conversation sentences from the conversation corpus.The conversational sentences are classified into four categories:information,question,directive,and commission.These classification label sequences are for analyzing the conversation progress and predicting the pecking order of the conversation.Ensemble of Bidirectional Encoder for Representation of Transformer(BERT),Robustly Optimized BERT pretraining Approach(RoBERTa),Generative Pre-Trained Transformer(GPT),DistilBERT and Generalized Autoregressive Pretraining for Language Understanding(XLNet)models are trained on conversation corpus with hyperparameters.Hyperparameter tuning approach is carried out for better performance on sentence classification.This Ensemble of Pre-trained Language Models with a Hyperparameter Tuning(EPLM-HT)system is trained on an annotated conversation dataset.The proposed approach outperformed compared to the base BERT,GPT,DistilBERT and XLNet transformer models.The proposed ensemble model with the fine-tuned parameters achieved an F1_score of 0.88.

A Hierarchical Combination Method for Computer Understanding of Chinese Language

On the basis of the characteristics of Chinese language such.as simple and uniform structure, distinct hierarchy and construction by word order and function words, and in the view of the human cognitive mechanism, a hierarchical combination method for computer understanding of Chinese language is put forward in this paper. By this method, the whole information of a sentence is hierarchically combined from the partial information of the basic units of it, with the unification operation under attribute description frames. This method is perfect in combining syntax analysis with semantic analysis, easy to implement, and very suitable for the computer understanding system for processing Chinese language.

A Rule Based System for Speech Language Context Understanding

Speech or Natural language contents are major tools of communication. This research paper presents a natural language processing based automated system for understanding speech language text. A new rule based model has been presented for analyzing the natural languages and extracting the relative meanings from the given text. User writes the natural language text in simple English in a few paragraphs and the designed system has a sound ability of analyzing the given script by the user. After composite analysis and extraction of associated information, the designed system gives particular meanings to an assortment of speech language text on the basis of its context. The designed system uses standard speech language rules that are clearly defined for all speech languages as English, Urdu, Chinese, Arabic, French, etc. The designed system provides a quick and reliable way to comprehend speech language context and generate respective meanings.

Chinese spoken language understanding in SHTQS

Spoken dialogue systems are an active research field with wide applications. But the differences in the Chinese spoken dialogue system are not as distinct as that of English. In Chinese spoken dialogues, there are many language phenomena. Firstly, most utterances are ill-formed. Secondly, ellipsis, anaphora and negation are also widely used in Chinese spoken dialogue. Determining how to extract semantic information from incomplete sentences and resolve negation, anaphora and ellipsis is crucial. SHTQS (Shanghai Transportation Query System) is an intelligent telephone-based spoken dialogue system providing information about the best route between any two sites in Shanghai. After a brief description of the system, the natural language processing is emphasized. Speech recognition sentences unavoidably contain errors. In language sequence processing procedures, these errors can be easily passed to the later parts and take on a ripple effect. To detect and recover these from errors as early as possible, language-processing strategies are specially considered. For errors resulting from divided words in speech recognition, segmentation and POS Tagging approaches that can rectify these errors are designed. Since most of the inquiry utterances are ill-formed and negation, anaphora and ellipsis are common language phenomena, the language understanding must be adequately adaptive. So, a partial syntactic parsing scheme is adopted and a chart algorithm is used. The parser is based on unification grammar. The semantic frame that extracts from the best arc set of the chart is used to represent the meaning of sentences. The negation, anaphora and ellipsis are also analyzed and corresponding processing approaches are presented. The accuracy of the language processing part is 88.39% and the testing result shows that the language processing strategies are rational and effective.

Language and Cross-Culture Understanding—Through Cross-Culture Study of the Word ‘Dragon’

This essay contributes to the analysis of the significance of cross-culture understanding in connection with language. It is important and necessary to promote cross- cultural understanding in order to communicate with people from various cultural backgrounds with the development of globalization. This essay also gives the example of the word‘dragon'to illustrate that the cross-culture understanding of language will make us communicate with each other more effectively.

A Natural Language Generation Algorithm for Greek by Using Hole Semantics and a Systemic Grammatical Formalism

This work is about the progress of previous related work based on an experiment to improve the intelligence of robotic systems,with the aim of achieving more linguistic communication capabilities between humans and robots.In this paper,the authors attempt an algorithmic approach to natural language generation through hole semantics and by applying the OMAS-III computational model as a grammatical formalism.In the original work,a technical language is used,while in the later works,this has been replaced by a limited Greek natural language dictionary.This particular effort was made to give the evolving system the ability to ask questions,as well as the authors developed an initial dialogue system using these techniques.The results show that the use of these techniques the authors apply can give us a more sophisticated dialogue system in the future.

Mirror neuron system as the joint from action to language

Mirror neuron system （MNS） represents one past decade, and it has been found to involve in multiple of the most important discoveries of cognitive neuroscience in the aspects of brain functions including action understanding, imitation, language understanding, empathy, action prediction and speech evolution. This manuscript reviewed the function of MNS in action understanding as well as language evolution, and specifically assessed its roles as the bridge from body language to fluent speeches. Then we discussed the speech defects of autism patients due to the disruption of MNS. Finally, given that MNS is plastic in adult brain, we proposed MNS targeted therapy provides an efficient rehabilitation approach for brain damages conditions as well as autism patients.

The Influence of a Cultural Dimension in Foreign Language Education

The connection between culture and language education is an important topic.This article is mainly to discuss the influence of the cultural dimension in foreign language education,including: the relationship between cultural and language,the role of cultural understanding in language learning;and the role of language unserstanding in learning about culture.

Evaluation on ChatGPT for Chinese Language Understanding

ChatGPT has attracted extension attention of academia and industry.This paper aims to evaluate ChatGPT in Chinese language understanding capability on 6 tasks using 11 datasets.Experiments indicate that ChatGPT achieved competitive results in sentiment analysis,summary,and reading comprehension in Chinese,while it is prone to factual errors in closed-book QA.Further,on two more difficult Chinese understanding tasks,that is,idiom fill-in-the-blank and cants understanding,we found that a simple chain-of-thought prompt can improve the accuracy of ChatGPT in complex reasoning.This paper further analyses the possible risks of using ChatGPT based on the results.Finally,we briefly describe the research and development progress of our ChatBIT.

On Task-based Language Teaching

This paper introduces task-based language teaching briefly,and presents an understanding of this pedagogy and someproblems in the application process.After having a good knowledge of task-based language teaching,English teachers can apply itinto the class,which can not be blindly of course.And it is important to consider the objective condition.

Recommender System for Information Retrieval Using Natural Language Querying Interface Based in Bibliographic Research for Naïve Users

With the increasing of data on the internet, data analysis has become inescapable to gain time and efficiency, especially in bibliographic information retrieval systems. We can estimate the number of actual scientific journals points to around 40,000 with about four million articles published each year. Machine learning and deep learning applied to recommender systems had become unavoidable whether in industry or in research. In this current, we propose an optimized interface for bibliographic information retrieval as a running example, which allows different kind of researchers to find their needs following some relevant criteria through natural language understanding. Papers indexed in Web of Science and Scopus are in high demand. Natural language including text and linguistic-based techniques, such as tokenization, named entity recognition, syntactic and semantic analysis, are used to express natural language queries. Our Interface uses association rules to find more related papers for recommendation. Spanning trees are challenged to optimize the search process of the system.

Promoting Intercultural Understanding in China’s High School ELT Class-rooms through Young Adult Literature

Language teaching involves not only developing students’linguistic performance but also promoting their intercultural competence.Within the intercultural competence framework,learners acquire skills that enable them to discover the complexity of various cultures and enhance intercultural understanding(IU).Young adult literature offers opportunities for English learners to en⁃counter cultural differences and creatively engage themselves with the diversity of the world.This paper firstly lists the worldwide calling for IU education.Then,it presents problems with IU education in English language teaching(ELT)classrooms in Chinese context.Finally,it provides a diversity of teaching approaches towards IU by means of young adult literature.

联合多意图识别与语义槽填充的双向交互模型

意图识别与语义槽填充是口语理解的两个主要任务,两者具有高度相关性,通常进行联合训练。随着口语理解任务的深入,研究发现用户在现实场景中的话语往往含有多个意图。但部分联合模型只能识别用户话语中的单个意图,未能充分建模多个意图和语义槽之间的关联性。考虑到话语中多个意图的信息可以引导语义槽填充,语义槽信息也可以帮助意图更好的识别,模型采用图注意力网络建立意图和语义槽之间的双向交互。具体的,将两个任务双向关联以便模型能够挖掘多个意图与语义槽之间的关系,同时引入两个任务的标签信息使模型能够学习到话语上下文和标签的关系,从而提高意图识别与语义槽填充的准确率,优化口语理解的整体性能。实验表明,模型在MixATIS和MixSNIPS两个多意图数据集上对比其他模型性能得到了显著提升。

诠释学视域下GPT语言模型的本质及特征

GPT语言模型的设计思路具有自然语言理解的诠释学思维特征,但本质上该模型并不具备诠释学语言理解的属人性基础;GPT语言模型将智能视为一种本体论层面以语言作为媒介的整体性系统“涌现”结果,缺乏诠释的“本体论—主体性”地位和“本体论—整体性”结构;GPT语言模型采用的生成式和预训练的设计思路凸显了诠释学理解和解释的“历史性”特征,而数据训练的强化和“思维链”的对话机制使该模型具有“效果历史”和“视域融合”的语言理解特征;GPT语言模型在模拟人类“偏见性”认知方面取得进步,但与人类的“偏见—个性化”和“偏见—创造性”能力相比存在根本差异;GPT语言模型形成了“诠释学循环”的语言对话机制,但其并不具有自身独立的“诠释学循环”实践基础,因此,难以达成诠释学意义上的语言理解共识。

语言学知识驱动的空间语义理解能力评测数据集研究

近20年来,深度学习技术显著提升了机器的自然语言处理能力,使之在诸多任务上接近甚至超过人类水平。机器学习的对象不再是直接来自人类语言学研究成果(知识),而是人类语言材料(数据)。在靠数据和算力驱动的大语言模型几近建成巴别塔的当下,语言学家通过深挖语言现象总结的语言学知识价值何在?本文提出从知识到数据的研究思路,设计了空间语义理解的6项任务:空间信息正误判别、异常空间信息识别、缺失参照成分补回、空间语义角色标注、空间表达异形同义判别、空间方位关系推理,以构建中文空间语义理解能力评测数据集为例,介绍从SpaCE2021到SpaCE2024数据集的设计思想、数据集制作概况以及机器在空间语义理解任务上的表现。总的来看,参加SpaCE赛事的大语言模型,在依赖表面分布特征(形式线索)的任务上容易获得好成绩,在依赖深层语义理解(认知能力)的任务上容易表现不好。因此,在人工智能高速发展使得语言学知识在计算机信息处理领域被动边缘化的当下,语言学知识的价值需要拓展,即用于指导小而精的高品质语言数据,以提升机器学习的效果和效率。为了计算应用的目的,语法研究应该在观察充分、描写充分、解释充分之上,追求更具挑战性的目标——生成充分。

大观念构建下的语言技能类课程思政教学探讨

课程思政建设的不断深入促使高校各专业积极探索更有效的教学方法。外语专业课程思政建设亦不断探索体系建设的有效路径和不同类别课程思政教学的最佳融入方式。根据外语专业人才培养要求与外语课程教学特点,英语专业技能类课程思政教学采用大观念驱动、理解为先的教学设计更能促进理解性学习的生成和思政概念由点及面的深入认识。通过构建大观念框架、丰富教学目标内涵、反向教学设计等步骤,技能类课程真正落实学生专业技能与思想认识的同步提升。

多模态隐喻视角下“理解当代中国”系列教材《高级中文读写教程》研究

本文立足多模态隐喻视角,聚焦“理解当代中国”系列教材的《高级中文读写教程》,从语境情境、主题内容、表征形式和应用实践四个层面构建多模态隐喻分析框架,深入研究教材的文字、图像、图表符号及空间关系等模态的隐喻意义,阐释其承载的信息传递、对话沟通、深化认知等功能,在多模态教学理念的指导下提出该教材的使用建议。研究发现:多模态隐喻意义的建构和识读有助于充分挖掘国际中文教材所蕴含的多要素融合的编写理念、多种模态搭配的叙事方式以及“中文·国情”融合的编写路径,实现教材语篇意义、表达与结构的三维统一。

如何测试ChatGPT的语义理解与常识推理水平?——兼谈大语言模型时代语言学的挑战与机会

ChatGPT等大语言模型在语义理解和常识推理方面表现优秀,其技术奥秘在于模型开发者在对词语进行向量表示时,遵循分布式语义学原理,采用了“嵌入”这种代数方法。但是,经典的测试语言运用等智能水平的“图灵测试”,难以识别欺骗和回避等作弊手段,因此,计算机科学家设计了“新图灵测试”,其中的威诺格拉德模式挑战与语言学关系密切。这一模式以代词消歧为测试点设计句子对和问题,但是经过大规模语料训练的语言模型可以凭借词汇上的统计相关性,而不是靠真正理解句子的意义来给出正确答案。为克服这一缺陷,学者们又发展出WinoGrande数据集,提高了数据的规模和难度,确保它们无法通过网络搜索等手段来得到正确答案。我们用威诺格拉德模式设计了无偏向双重句子对测试ChatGPT,展示了大语言模型在语义理解和常识推理方面已达到接近人类的水平。当然,从具身模拟假说来看,大语言模型不可能像人一样富有体验性地理解人类自然语言。最后,我们呼吁:语言学家应该积极参与构建WinoGrade测试集之类的工作,在人工智能时代扩展自身的学术研究领域。

新时代来华留学生国情教育融会“理解当代中国”目标的有益尝试

高等学校“理解当代中国”国际中文系列教材将中文学习与当代中国国情教育相结合,引领来华留学生读懂中国理论、了解中国实践、表达世界观点,在学习语言的同时,加深文化理解,让来华留学生能够更加准确地向世界阐释中国方案、中国智慧和中国理念。南京大学以高级汉语水平的语言进修生和本科学历生为主要授课对象,积极探索高等学校“理解当代中国”国际中文系列教材在语言文化课堂、国情教育课堂和社会实践课堂中的使用方法,打破课程壁垒,发挥三类课程的协同育人作用,打开各国学生读懂当代中国的“思想之窗”,从而推动来华留学生国情教育体系建设,提升来华留学生讲好中国故事的能力。

软件标识符的自然语言规范性研究

软件源代码的理解是软件协同开发与维护的核心,而源代码中占半数以上的标识符的理解则在软件理解中起到重要作用,传统软件工程主要研究通过命名规范限制标识符的命名过程以构造更易理解和交流的标识符。该文在梳理分析常见编程语言命名规范的基础上,该文提出一种全新的标识符可理解性评价标准。具体而言,首先总结梳理了常见主流编程语言中的命名规范并类比自然语言语素概念,提出基于软件语素的标识符构成过程,即标识符的构成可被视为软件语素的生成、排列和连接过程;在此基础上,该文提出一种结合自然语料库的软件标识符规范性评价方法,用来衡量软件标识符是否易于理解;最后,通过源代码理解数据集和Github平台中开源项目对规范性指标进行了验证性实验,结果表明,该文所提出的规范性分数能够很好衡量软件项目的可理解性。