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 requir...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.展开更多
As an indispensable part of medical practice.doctor-patient conversation is drawing more and more attention in the field of sociology,psychology and linguistics.Little attention,however,has been paid to the interperso...As an indispensable part of medical practice.doctor-patient conversation is drawing more and more attention in the field of sociology,psychology and linguistics.Little attention,however,has been paid to the interpersonal aspect of the conversation between doctors and patients,which is regarded as one of the most complex interpersonal relationships.Being dominant in the interaction,doctors'words,mainly in the form of questions,deserve more studies,especially for the interpersonal functions delivered.This study mainly focuses on this aspect.展开更多
In the framework of metaphor theory,this paper tries to research conversions from animal nouns to verbs in English,and aims to help English learners to better understand this language phenomenon.First the paper explor...In the framework of metaphor theory,this paper tries to research conversions from animal nouns to verbs in English,and aims to help English learners to better understand this language phenomenon.First the paper explores the motivations of this language phenomenon,and points out that verbs converted from animal nouns can help enrich the vocabulary,make the expression vivid and concise,and achieve certain pragmatic effects as well.Second,it studies the mechanisms of the conversion by analyzing the mapping process and similarities between two domains.Last it points out the importance of the context and the geographic factor in understanding verbs converted from animal nouns.展开更多
Google's Bard has emerged as a formidable competitor to OpenAI's ChatGPT in the field of conversational AI.Notably,Bard has recently been updated to handle visual inputs alongside text prompts during conversat...Google's Bard has emerged as a formidable competitor to OpenAI's ChatGPT in the field of conversational AI.Notably,Bard has recently been updated to handle visual inputs alongside text prompts during conversations.Given Bard's impressive track record in handling textual inputs,we explore its capabilities in understanding and interpreting visual data(images)conditioned by text questions.This exploration holds the potential to unveil new insights and challenges for Bard and other forthcoming multi-modal Generative models,especially in addressing complex computer vision problems that demand accurate visual and language understanding.Specifically,in this study,we focus on 15 diverse task scenarios encompassing regular,camouflaged,medical,under-water and remote sensing data to comprehensively evaluate Bard's performance.Our primary finding indicates that Bard still struggles in these vision scenarios,highlighting the significant gap in vision-based understanding that needs to be bridged in future developments.We expect that this empirical study will prove valuable in advancing future models,leading to enhanced capabilities in comprehending and interpreting finegrained visual data.Our project is released on https://github.com/htqin/GoogleBard-VisUnderstand.展开更多
文摘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.
文摘As an indispensable part of medical practice.doctor-patient conversation is drawing more and more attention in the field of sociology,psychology and linguistics.Little attention,however,has been paid to the interpersonal aspect of the conversation between doctors and patients,which is regarded as one of the most complex interpersonal relationships.Being dominant in the interaction,doctors'words,mainly in the form of questions,deserve more studies,especially for the interpersonal functions delivered.This study mainly focuses on this aspect.
文摘In the framework of metaphor theory,this paper tries to research conversions from animal nouns to verbs in English,and aims to help English learners to better understand this language phenomenon.First the paper explores the motivations of this language phenomenon,and points out that verbs converted from animal nouns can help enrich the vocabulary,make the expression vivid and concise,and achieve certain pragmatic effects as well.Second,it studies the mechanisms of the conversion by analyzing the mapping process and similarities between two domains.Last it points out the importance of the context and the geographic factor in understanding verbs converted from animal nouns.
文摘Google's Bard has emerged as a formidable competitor to OpenAI's ChatGPT in the field of conversational AI.Notably,Bard has recently been updated to handle visual inputs alongside text prompts during conversations.Given Bard's impressive track record in handling textual inputs,we explore its capabilities in understanding and interpreting visual data(images)conditioned by text questions.This exploration holds the potential to unveil new insights and challenges for Bard and other forthcoming multi-modal Generative models,especially in addressing complex computer vision problems that demand accurate visual and language understanding.Specifically,in this study,we focus on 15 diverse task scenarios encompassing regular,camouflaged,medical,under-water and remote sensing data to comprehensively evaluate Bard's performance.Our primary finding indicates that Bard still struggles in these vision scenarios,highlighting the significant gap in vision-based understanding that needs to be bridged in future developments.We expect that this empirical study will prove valuable in advancing future models,leading to enhanced capabilities in comprehending and interpreting finegrained visual data.Our project is released on https://github.com/htqin/GoogleBard-VisUnderstand.
