Leveraging Vision-Language Pre-Trained Model and Contrastive Learning for Enhanced Multimodal Sentiment Analysis

Multimodal sentiment analysis is an essential area of research in artificial intelligence that combines multiple modes,such as text and image,to accurately assess sentiment.However,conventional approaches that rely on unimodal pre-trained models for feature extraction from each modality often overlook the intrinsic connections of semantic information between modalities.This limitation is attributed to their training on unimodal data,and necessitates the use of complex fusion mechanisms for sentiment analysis.In this study,we present a novel approach that combines a vision-language pre-trained model with a proposed multimodal contrastive learning method.Our approach harnesses the power of transfer learning by utilizing a vision-language pre-trained model to extract both visual and textual representations in a unified framework.We employ a Transformer architecture to integrate these representations,thereby enabling the capture of rich semantic infor-mation in image-text pairs.To further enhance the representation learning of these pairs,we introduce our proposed multimodal contrastive learning method,which leads to improved performance in sentiment analysis tasks.Our approach is evaluated through extensive experiments on two publicly accessible datasets,where we demonstrate its effectiveness.We achieve a significant improvement in sentiment analysis accuracy,indicating the supe-riority of our approach over existing techniques.These results highlight the potential of multimodal sentiment analysis and underscore the importance of considering the intrinsic semantic connections between modalities for accurate sentiment assessment.

Compare and Contrast the Strengths and Weaknesses of Task-based Language Teaching Method and Audio Lingual Teaching Method and Study How They Impact Upon Both the Teachers and the Learners

This essay focused on comparing and contrasts audio lingual method and Task-based language teaching method,demonstrating the strengths and weaknesses of the two methods and discusses how they impact upon both the teachers and the learners.TBLT is the contemporary teaching method with a great number of strong points.It seems that if there is more relatively scientific and effective method,TBLT should give way to it.The ultimate goal is to advocate the comparatively successful teaching method in the field of foreign language education.

Contrastive Rhetoric and Inter-language Theory

A learner’s stages of L2 development are connected by his or her L1 and culture.It is,accordingly,of paramount importance to understand the second language learners’culture and learning process and better assist them through this process in the way of teaching them English.Similarly,inter-language theory(IL)and contrastive rhetoric are affected by factors,such as learner’s L1,learning experiences,and culture.This paper talks about these two theory’s characteristics,constructs,and importance,so language instructors may better understand the L2 learning phenomena and think out better methods to help language learners improve their language skills.

Basic Contrast of English, Chinese and the Shui language Sentences

This paper is to explore the similar and different syntactic linear patterns and thought modals among English, Chinese and the Shui language, discussing this topic from microscopic, giving a much detailed contrast and comparison on microscopic English, Chinese and the Shui language syntactic.

Macroscopic Contrast of English,Chinese and the Shui Language Syntactic Features and Thought Patterns

This paper is to explore the syntactic features and thought patterns among English,Chinese and the Shui language,discussing this topic from macroscopic,giving a brief contrast and comparison on macroscopic of English,Chinese and the Shui language.

Study of Second Language Writing:Contrastive Rhetoric——Cultural Thought P atterns in Inter-cultural Education by Robert B.Kaplan

Contrastive rhetoric is an area of research in second language acquisition that identifies problems in composition en?countered by second language writers and, by referring to the rhetorical strategies of the first language, which attempts to explain them.The article'Cultural Thought Patterns in Inter-cultural Education by Robert B.Kaplan' indicates that different ways of under?standing language and writing styles.The English style is linear while the oriental is spiral.The author gives suggestions how to help those from other cultures learn how to write in acceptable academic English.However,he points out this is not because it is the cor?rect way but because it helps the students attain their goal of academic success.A student can write an adequate essay in his native language,can he write an adequate essay in a second language? In the following paragraphs,I will go to the details to present the lit?erature review,some case analysis,discussion and so on. It's about the contractive rhetoric based on the article of Robert B.Kaplan, then give conclusion.

A Contrastive Study of the Relationship between Language and Culture Exemplified in English and Chinese

This paper analyses the relationship between language and culture exemplified in English and Chinese.Language is not only a part of culture but also the carrier and container of culture.As a mirror of culture language is strongly influenced by culture.Language also exerts its influence on culture.Language and culture is closely related.

Politeness Language in English and Chinese

Politeness is a very important topic in modern linguistics. With the development of the study of linguistics, a lot of people lay the emphasis on the study of politeness language. It can be observed in all languages and cultures as a social phenomenon, which plays an important role in individuals' linguistic behavior. But due to different culture, different politeness principles and other reasons, different peo ple have different understandings about the definition of politeness, and they deal with politeness in different ways. Through different study background, principle and theory, this paper focuses on the politeness language in English and Chinese culture caused the differences. Politeness languages are mainly expressed in the forms of address, greeting, congratulation, appreciation and apology . The biggest difference between English and Chinese is that the Chinese system is profusion and complicated, but the English one is quite simple. Age, income, marriage and religion, etc. are taboos in English and people in western countries like to talk about weather when they meet each other; greeting in Chinese almost includes different things about one's life. Appreciation in Chinese is not used frequently. Peo ple do not often say "thank you" to their families or friends. Appreciation in English can be used among families, friends, and colleagues and so on. At the end of this thesis, a conclusion is drawn; limitations and some suggestions of the research are stated!

A Theoretical Study of Language Transfer

This paper introduces some theories concerning language transfer, such as behaviorist views and cognitive views, and the relationship between language transfer and contrastive and error analysis, interlanguage, fossilization, universial grammar. It is believed that incorporating the concept of language transfer in English teaching may facilitate students’ learning of the language.

Crosslinguistic Influence of the First Language: Interlingual errors in the writing of ESL Saudi learners

Writing as a productive skill is challenging for the learners as it requires rich linguistic and cognitive abilities to match words with ideas well. A learner of any language as Second Language (SL), for example English, Arabic, Chinese, or German, enters an early, natural, and inevitable stage of making various errors in writing compositions. The errors could be due to dissimilar linguistic systems of the two languages (L1 and L2) or the lack of understanding the SL linguistic rules. This study focuses on investigating the frequent and common inter-lingual errors (the negative influence) committed by Arabic-speaking learners of English as Second Language (ESL). The study is based on Error Analysis (EA) of the essays of a group of English major undergraduate students from the University of Bisha, Al-Namas, Saudi Arabia. Contrastive Analysis Hypothesis (CAH)is included to predict most of the errors in the collected samples. The findings of the study show that common errors in the data are interlingual (54.03%). The errors within the Grammatical Category include the misuse of tenses, dropping the subjects, dropping verb to be-copular and word order misplacement. The majority of errors in the study are due to the interference of L1 linguistic system including the different orthographic, phonological,morpho-syntactic components. For instance, the participants’ dropping subjects in English can be attributed to the fact that Arabic as L1 and as Null Subject Language (NSL), unlike English, allows subject drop. The results also find that within the Lexical Category,preposition and article errors are the most frequent errors and that spelling errors are the most dominant errors within the Mechanics Category. The study gives some pedagogical implications and suggestions to avoid and minimize the interlingual errors of such type. For instance, teachers need to focus more on enlightening their students about the contrastive language systems from the early stage of learning ESL.

引入知识增强和对比学习的知识图谱补全

知识图谱补全是提高知识图谱质量的重要手段,主要分为基于结构和基于描述的方法。基于结构的补全方法对图谱中常见的长尾实体推理性能表现不佳,基于描述的补全方法在描述信息利用和负样本信息学习方面存在不足。针对上述问题,提出基于知识增强的知识图谱补全方法KEKGC。设计一种特定模板,将三元组及其描述信息通过人工定义的模板转换为连贯的自然语言描述语句输入预训练语言模型,增强语言模型对三元组结构知识与描述知识的理解能力。在此基础上,提出一种对比学习框架来提高链接预测任务的效率与准确率,通过建立记忆库存储实体嵌入向量,从中选择正负样本并结合Info NCE损失进行训练。实验结果显示,相较于MEM-KGC,KEKGC在WN18RR数据集上链接预测任务的平均倒数秩(MRR)提升了5.5,Hits@1、Hits@3、Hits@10指标分别提升了2.8、0.7、4.2个百分点,三元组分类任务准确率达到94.1%,表明所提方法具有更高的预测准确率与更好的泛化能力,尤其对于长尾实体,能够有效提升图谱补全的效果与效率。

语义增强图像-文本预训练模型的零样本三维模型分类

目前,基于对比学习的图像-文本预训练模型(CLIP)在零样本3维模型分类任务上表现出了巨大潜力,然而3维模型和文本之间存在巨大的模态鸿沟,影响了分类准确率的进一步提高。针对以上问题,该文提出一种语义增强CLIP的零样本3维模型分类方法。该方法首先将3维模型表示成多视图;然后为了增强零样本学习对未知类别的识别能力,通过视觉语言生成模型获得每张视图及其类别的语义描述性文本,并将其作为视图和类别提示文本之间的语义桥梁,语义描述性文本采用图像字幕和视觉问答两种方式获取;最后微调语义编码器将语义描述性文本具化为类别的语义描述,其拥有丰富的语义信息和较好的可解释性,有效减小了视图和类别提示文本的语义鸿沟。实验表明,该文方法在ModelNet10和ModelNet40数据集上的分类性能优于现有的零样本分类方法。

以对比学习与时序递推提升摘要泛化性的方法

为了有效缓解基于交叉熵损失函数训练的传统文本摘要模型所面临的推理过程中性能下降、泛化性较低、生成过程中曝光偏差现象严重、生成的摘要与参考摘要文本相似度较低等问题,提出了一种新颖的训练方式,一方面,模型本身以beamsearch的方式生成候选集,以候选摘要的评估分数选取正负样本,在输出的候选集中以“argmax-贪心搜索概率值”和“标签概率值”构建2组对比损失函数;另一方面,设计作用于候选集句内的时序递推函数引导模型在输出每个单独的候选摘要时确保时序准确性,并缓解曝光偏差问题。实验表明,所提方法在CNN/DailyMail和Xsum公共数据集上的泛化性得到提升,Rouge与BertScore在CNN/DailyMail上达到47.54和88.51,在Xsum上达到了48.75和92.61。

基于CLIP和双空间自适应归一化的图像翻译

现有的图像翻译方法大多依赖数据集域标签来完成翻译任务,这种依赖往往限制了它们的应用范围。针对完全无监督图像翻译任务的方法能够解决域标签的限制问题,但是普遍存在源域信息丢失的现象。为了解决上述2个问题,提出一种基于对比学习语言-图像预训练(CLIP)的无监督图像翻译模型。首先,引入CLIP相似性损失对图像的风格特征施加约束,以在不使用数据集域标签的情况下增强模型传递图像风格信息的能力和准确性;其次,对自适应实例归一化(AdaIN)进行改进,设计一个新的双空间自适应归一化(DSAdaIN)模块,在特征的风格化阶段添加网络的学习和自适应交互过程,以加强对内容源域信息的保留;最后,设计一个鉴别器对比损失来平衡对抗网络损失的训练和优化过程。在多个公开数据集上的实验结果表明,与Star GANv2、Style DIS等模型相比,该模型可在准确传递图像风格信息的同时保留一定的源域信息,且在定量评估指标FID分数和KID分数上分别提升了近3.35和0.57×102,实现了较好的图像翻译性能。

基于对比学习的大型语言模型反向词典任务提示生成方法

反向词典任务是一种新兴的任务,目的是根据给定的定义来查找对应的单词。大规模语言模型为这一任务提供了新的可能性,但是提示语句的质量会影响大模型的性能。为此,提出了一种基于对比学习的提示生成方法。该方法在从多个语义层面上理解定义语义的同时,还利用对比学习的原理在训练过程中引入了负例,提升了模型的泛化能力。通过这种方法,可以将目标单词缩小到一个小范围内,然后用大模型从这个范围内选择最符合定义语义的单词。实验结果表明,该方法可以有效地提升大规模语言模型在反向词典任务上的表现。提示生成模型有94.7%的概率生成包含目标词的范围,大规模语言模型有58.03%的概率直接选出目标单词,有74.55%的概率在给出5个候选单词时包含目标单词。

基于对比学习与语言模型增强嵌入的知识图谱补全

知识图谱是由各种知识或数据单元经过抽取等处理而组成的一种结构化知识库,用于描述和表示实体、概念、事实和关系等信息。自然语言处理技术的限制和各种知识或信息单元文本本身的噪声都会使信息抽取的准确性受到一定程度的影响。现有的知识图谱补全方法通常只考虑单一结构信息或者文本语义信息,忽略了整个知识图谱中同时存在的结构信息与文本语义信息。针对此问题,提出一种基于语言模型增强嵌入与对比学习的知识图谱补全(KGC)模型。将输入的实体和关系通过预训练语言模型获取实体和关系的文本语义信息,利用翻译模型的距离打分函数捕获知识图谱中的结构信息,使用2种用于对比学习的负采样方法融合对比学习来训练模型以提高模型对正负样本的表征能力。实验结果表明,与基于来自Transformеr的双向编码器表示的知识图谱补全(KG-BERT)模型相比,在WN18RR和FB15K-237数据集上该模型链接预测的排名小于等于10的三元组的平均占比(Hits@10)分别提升了31%和23%,明显优于对比模型。