Dialogue Text Research Articles

The Impact of a Dialogic Intervention on a Chinese Rural Teacher and Students' Stances Towards Texts

ABSTRACTIn language arts classrooms, dialogic teaching encourages diverse text interpretations, fostering discussions that enhance students' literacy skills like reasoning. However, adopting a dialogic stance poses challenges for both teachers and students. The study investigated the impact of a tailor‐made dialogic intervention on a Chinese rural teacher and students' stances towards texts. Chinese rural students shifted from efferent and instrumental stances to a more critical‐analytical stance, indicating a profound change in how rural students interacted with texts through exploratory talk, highlighting the potential of dialogic teaching practices to foster deep engagement with reading materials. However, after the intervention ended, the teacher reverted to a monologic approach due to limited dialogic texts and time constraints. The findings underscore the potential of dialogic teaching for engaging rural students, yet highlight the need for sustained teacher training and suitable texts. Future research should focus on cultivating and maintaining students' growth mindset effectively in rural settings.

CoMaSa:Context Multi-aware Self-attention for emotional response generation

Dialogue generation is an important research direction in natural language generation, and generating utterances with appropriate contextual emotion is a challenging task. The previous work incorporates commonsense knowledge as an auxiliary information source by simply concatenating or adding it to the embeddings of the dialogue context. They did not consider updating the commonsense knowledge from the perspective of context semantic information or selecting context from the perspective of commonsense knowledge semantic information. At the same time, they did not extract emotional information from dialogue context and commonsense knowledge to enhance the embeddings of the dialogue text. These defects may lead to the dialogue model’s inability to judge the semantic consistency between context and generated utterance, and result in the generation of utterance unassociated with the dialogue context and the corresponding context emotion being untrue. Our goal is to build an emotional dialogue generation model that can deeply integrate commonsense knowledge with dialogue context and achieve bidirectional semantic interaction and enhancement. Therefore, in this paper, we propose a context multi-aware self-attention emotion dialogue generation model that models from multiple aspects such as the semantic information, emotional information of context and external knowledge, it effectively alleviates the problems of generating utterance unrelated to the context and disordered emotional expressions in utterance. First, we adopt the transformer encoder as the basic framework and carefully construct a context-aware encoder capable of deeply extracting semantic information. Specifically, we ingeniously integrate different levels of positional information to simulate the natural flow of conversation between speakers. Then, we effectively integrate these positional information with the basic word embedding vectors, and enrich the dialogue’s embedding representation. Lastly, by introducing the attention mechanism, the model can precisely capture and extract context information closely related to the current situation and achieve a deep understanding and accurate grasp of the dialogue content. Second, we consider that the relationship between context and commonsense knowledge is not just a unidirectional selection but a more intimate bidirectional interaction. Therefore, we designed a bidirectional interaction attention component for context and external knowledge. Specifically, we use the method of iterative similarity matrices to calculate the bidirectional similarity relationship between context and external knowledge. Then, using the attention algorithm, we calculate the connectivity from context to commonsense knowledge and from commonsense knowledge to context. Finally, by combining the semantic embeddings of both aspects, we achieve bidirectional updating of context and external knowledge. Third, we establish an emotion-aware decoder based on multi-task learning, which includes an emotion classifier and an utterance generator. In the emotion classifier, we establish an auxiliary task for emotion recognition to help the model understand the process of emotional transition. We use the output of the emotion recognition model as an additional supervisory signal to better control the generation of emotional responses. In the utterance generator, we integrate the output of the emotion classifier with the interaction vector from the bidirectional interaction attention component. This fusion allows us to generate utterance that aligns with the emotional context. Our experiments on DailyDialog and ESConv datasets show that CoMaSa outperforms the baselines in terms of Perplexity, Distinct-1, Distinct-2, and human evaluation. The experiment demonstrates the effectiveness of our Context Multi-aware Self-attention model in the task of emotion generation.

A typology of the Arabic system of mood

Abstract This article is about the interpersonal side of the clause in Arabic. It more specifically studies all the different speech functions inherent in the exchange of meanings: offer, command (viz. proposals), statement, and question (viz. propositions). The main question that this article seeks to answer is how Arabic realizes these different speech functions through variations in mood choices. The purpose of the article is to fill a gap in the literature. In fact, there have not been many major attempts to describe the Arabic system of mood in the reference grammars of Arabic. Although this article defines the delimitation of the system of mood in Arabic from the perspective of systemic functional linguistics, it also draws from the work of earlier Arab grammarians which has been either ignored or misinterpreted for centuries. For the purpose of this study, dialogic texts from various sources have been analysed including scenes from movies, plays, novels, and The Noble Quran. The findings show that the system of mood in Arabic is different from its English counterpart and from that of other languages.

Renewal of Modern Arabic Poetry: The Culture-Creativity Controversy

The present research raises critical issues regarding modern Arabic poetry, wondering about the limits of creativity starting from awareness about poetry writing to its reading prospects. It observes how the Arab poet is torn between focusing on the depth of culture (with what it imposes in terms of conforming to the prevailing poetic model) and between the temptations of creativity, which requires a level audacity and aspiration to different and renewed written models. The research disassembles the structure of poetry as a cultural system rooted in the Arab memory and discusses the question of identity in its relationship to creativity. It stresses the centrality of the traditional Arab poetic model while pointing to attempts at transcending such model. The most prominent of these attempts was that accomplished by the Arab poetic modernity movement in the forties and fifties of the last century (free poetry and prose poem). The research explores the rationale behind this decisive shift in the history of Arabic poetry at the levels of its creative and cultural foundations. In this regard, the research raised the question of leadership, the call for renewal, and defense of the pioneering poets of their innovative achievement against the successive creative booms. The research also discussed the controversy over tradition versus creativity, the question of dialogue in texts, the prospects for critical poetry reading with its scientific and cultural transformations, and the distribution of its theoretical and methodological references between Arab heritage and Western culture.

COLLOQUIAL REGISTER IN THE PRAGMATICS OF A LITERARY TEXT

The purpose of the article is a linguistic analysis of communicative registers based on the material of works of art in the Kazakh language. The communicative register is a type of information represented in the text and allows you to judge: the statics or dynamics of what is happening (reproductive-descriptive and reproductive-narrative registers); the speaker’s position in relation to action: inside events (reproductive), distanced from events (informative, generative registers); the speaker’s intentions: expression of will (voluntary register) or reaction to the interlocutor’s remark, as well as the situation as a whole (reactive register). The communicative register is a type of information represented in the text and allows you to judge: the statics or dynamics of what is happening (reproductive-descriptive and reproductive-narrative registers); the speaker’s position in relation to action: inside events (reproductive), distanced from events (informative, generative registers. O. V. Evstafiadi compares directional and non-directional communicative registers, highlighting in the communicative register such quality as addressability. A person strives not only for mutual understanding when using linguistic symbols, but also to evoke a reaction from the listener to the information expressed by him. All these phenomena are realized in the speech act. In the mind of the speaker, based on internal and external factors of society, a communicative goal arises, the purpose of which is to influence the actions, attitude of the addressee or determine the essence of a specific problem. The choice of certain linguistic means and linguistic structures is based on the pragmatics of the dialogue text, the communicative goal of the speaker, his own opinion, approach, and the general level of knowledge of the communicants, depending on specific circumstances. Keywords: speech registers, communicative units, linguistic stylistics, speech activity, pragmatics.

Advancement of Generative Pre-trained Transformer Chatbots in Answering Clinical Questions in the Practical Rhinoplasty Guideline.

The Generative Pre-trained Transformer (GPT) series, which includes ChatGPT, is an artificial large language model that provides human-like text dialogue. This study aimed to evaluate the performance of artificial intelligence chatbots in answering clinical questions based on practical rhinoplasty guidelines. Clinical questions (CQs) developed from the guidelines were used as question sources. For each question, we asked GPT-4 and GPT-3.5 (ChatGPT), developed by OpenAI, to provide answers for the CQs, Policy Level, Aggregate Evidence Quality, Level of Confidence in Evidence, and References. We compared the performance of the two types of artificial intelligence (AI) chatbots. A total of 10 questions were included in the final analysis, and the AI chatbots correctly answered 90.0% of these. GPT-4 demonstrated a lower accuracy rate than GPT-3.5 in answering CQs, although without statistically significant difference (86.0% vs. 94.0%; p = 0.05), whereas GPT-4 showed significantly higher accuracy for the level of confidence in Evidence than GPT-3.5 (52.0% vs. 28.0%; p < 0.01). No statistical differences were observed in Policy Level, Aggregate Evidence Quality, and Reference Match. In addition, GPT-4 rated significantly higher in presenting existing references than GPT-3.5 (36.9% vs. 24.1%; p = 0.01). The overall performance of GPT-4 was similar to that of GPT-3.5. However, GPT-4 provided existing references at a higher rate than GPT-3.5. GPT-4 has the potential to provide a more accurate reference in professional fields, including rhinoplasty. This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266 .

Assessing language models’ task and language transfer capabilities for sentiment analysis in dialog data

Our work explores the differences between GRU-based and transformer-based approaches in the context of sentiment analysis on text dialog. In addition to the overall performance on the downstream task, we assess the knowledge transfer capabilities of the models by applying a thorough zero-shot analysis at task level, and on the cross-lingual performance between five European languages. The ability to generalize over different tasks and languages is of high importance, as the data needed for a particular application may be scarce or non existent. We perform evaluations on both known benchmark datasets and a novel synthetic dataset for dialog data, containing Romanian call-center conversations. We study the most appropriate combination of synthetic and real data for fine-tuning on the downstream task, enabling our models to perform in low-resource environments. We leverage the informative power of the conversational context, showing that appending the previous four utterances of the same speaker to the input sequence has the greatest benefit on the inference performance. The cross-lingual and cross-task evaluations have shown that the transformer-based models possess superior transfer abilities to the GRU model, especially in the zero-shot setting. Considering its prior intensive fine-tuning on multiple labeled datasets for various tasks, FLAN-T5 excels in the zero-shot task experiments, obtaining a zero-shot accuracy of 51.27% on the IEMOCAP dataset, alongside the classical BERT that obtained the highest zero-shot accuracy on the MELD dataset with 55.08%.

Channel-Aware Decoupling Network for Multiturn Dialog Comprehension.

Training machines to understand natural language and interact with humans is one of the major goals of artificial intelligence. Recent years have witnessed an evolution from matching networks to pretrained language models (PrLMs). In contrast to the plain-text modeling as the focus of the PrLMs, dialog texts involve multiple speakers and reflect special characteristics, such as topic transitions and structure dependencies, between distant utterances. However, the related PrLM models commonly represent dialogs sequentially by processing the pairwise dialog history as a whole. Thus, the hierarchical information on either utterance interrelation or speaker roles coupled in such representations is not well addressed. In this work, we propose compositional learning for holistic interaction across the utterances beyond the sequential contextualization from PrLMs, in order to capture the utterance-aware and speaker-aware representations entailed in a dialog history. We decouple the contextualized word representations by masking mechanisms in transformer-based PrLM, making each word only focus on the words in the current utterance, other utterances, and two speaker roles (i.e., utterances of the sender and utterances of the receiver), respectively. In addition, we employ domain-adaptive training strategies to help the model adapt to the dialog domains. Experimental results show that our method substantially boosts the strong PrLM baselines in four public benchmark datasets, achieving new state-of-the-art performance over previous methods.

Efforts to Improve Student Learning Outcomes Reading English Dialogue Text with Role Playing Method in Class IX-5 SMP Negeri 17 South Tangerang City

Efforts to Improve Student Learning Outcomes Reading English Dialogue Text with Role Playing Method in Class IX-5 SMP Negeri 17 South Tangerang City

Female Masculinity in Jungle Cruise Film (2021)

This research aims to find out the characterization of the main female character and how the character represents female masculinity in the film Jungle Cruise. Researcher analyzed the film through the masculinity theory approach by Peter Lehman and female masculinity by Judith Halberstam. Researcher used qualitative method with descriptive analysis to find findings in the research. The data used is dialogue text and photo scene footage in the Jungle Cruise film. In the characterization analysis, it was found that Dr. Lily has the characteristics of being smart, clever, kind, caring and ambitious. The value of masculinity in Dr. Lily is characterized by the elements of power, courage, heroism, and leadership. In line with Judith Halberstam's theory of female masculinity (1998), which says that female masculinity refers to a woman's body that displays masculine performatives which can be seen from the way she behaves and her physical appearance.

The Contrastive and Referential Function of Specific Classifiers in Xiamen Southern Min—Evidence from a Cognitive Experimental Study

Southern Min is generally known for not using classifiers [CL] for expressing definiteness/indefiniteness as it is associated with the bare classifier construction [CL N]. This paper offers evidence from Xiamen Southern Min (XSM) that the use of a specific classifier vs. the general classifier é contributes to referentiality in an alternative way by supporting object identification as it is due to the semantic specificity present in specific classifiers and absent in the general classifier. In a dialogic cognitive experiment adapted from the “Hidden color-chips” task (Enfield and Bohnemeyer 2001), 18 participants had to manipulate their addressees’ attention toward various objects situated in their immediate physical space through language as well as deictic gestures. The objects were associated with different specific classifiers or with the general classifier, and they were arranged according to the factors of (a) distance from speaker, (b) visibility for speaker, and (c) uniqueness (adjacency of similar items). The results show, among other things, that there is a higher tendency to use the specific CL in the [demonstrative CL N] construction if adjacent similar objects [−unique] are too far away from the speaker for clear identification by a demonstrative or a pointing gesture. This is seen as a last-resort strategy for creating contrast. Further corroboration comes from the use of specific classifiers in later mentions after the general CL failed to achieve clear identification. These findings can be situated in the broader context of other languages with classifiers in contrastive function (Thai, Vietnamese, and Ponapean) and they show the relevance of using dialogic texts for modeling classifier selection in contrast to narrative texts. Finally, dialogic contexts may serve as bridging contexts for grammaticalization from numeral classifiers to definiteness markers.

Author and narrator in fanfiction community texts (a study of fanfics based on literary classics)

Recent research on Russian-language fanfiction has focused on analyzing the fanfic text itself, exploring its structural, stylistic, and artistic aspects. While the role of the author in the fanfiction community has always been crucial, the current research emphasizes a more comprehensive investigation of the sociological dimension of authorship rather than its literary aspects. The unique communication style prevalent in the fanfiction community challenges the traditional model of author-reader relations, leading to a shift in the dynamics of interaction between ficwriters and ficreaders. This change creates a noticeable gap in the academic understanding of the different forms of authorship in fanfiction. To maintain a meaningful author–reader–original text dialogue, ficwriters introduce a specific narrator persona. Our study, which examines narrators in fanfics based on Russian classical literature through comparative analysis with metatextual information and insights from in-depth interviews, concludes that the narrator possesses both obligatory and optional features. These features enable the narrator to act as an intratextual mediator in the ongoing dialogue between the author and the reader. The narrator seamlessly integrates into the fictional world as a character while conveying the author’s perspective outside the text. This role significantly differs from the conventional concept of a “raisonneur.” The analysis of the narrative structure reveals that the narrator in fanfiction serves as an artistic projection of the fanfic author, blurring the lines between biographical and artistic authorship for both readers and the author. The narrator actively contributes to the storytelling process, providing synchronous commentary on their own work and exercising control over the characters. This aligns with the authorial intention in fanfiction, where the author initiates a dialogue between readers and the classic source text, both “from inside” and “outside” the text through creative interpretation.

Kemerdekaan Pendididikan Anak Pedalaman (Analisis Semiotika Tentang Pendidikan Yang Membebaskan Dalam Film Sokola Rimba (2013)

This research is based on the fact that education in Indonesia is not distributed evenly, especially in areas that fall into the 3 category (Outermost, Remote and Disadvantaged). The community does not yet have adequate access and facilities to carry out teaching and learning activities. The aim of this research is to understand and explain the semiotic analysis of liberating education in the film Sokola Rimba (2013). The author uses qualitative research methods and semiotic analysis according to the Charles Sanders Pierce model. This semiotic model focuses on icons, indices and symbols that appear in the film Sokola Rimba. The findings from this research are that the interpretation of dialogue text, images and videos from the film is able to depict a social reality. This social reality is a picture of educational inequality and the struggle of the figures to gain educational independence. It is hoped that this article can help the government to be more responsive in solving problems surrounding educational independence in rural communities.

DialGNN: Heterogeneous Graph Neural Networks for Dialogue Classification

Dialogue systems have attracted growing research interests due to its widespread applications in various domains. However, most research work focus on sentence-level intent recognition to interpret user utterances in dialogue systems, while the comprehension of the whole documents has not attracted sufficient attention. In this paper, we propose DialGNN, a heterogeneous graph neural network framework tailored for the problem of dialogue classification which takes the entire dialogue as input. Specifically, a heterogeneous graph is constructed with nodes in different levels of semantic granularity. The graph framework allows flexible integration of various pre-trained language representation models, such as BERT and its variants, which endows DialGNN with powerful text representational capabilities. DialGNN outperforms on CM and ECS datasets, which demonstrates robustness and the effectiveness. Specifically, our model achieves a notable enhancement in performance, optimizing the classification of document-level dialogue text. The implementation of DialGNN and related data are shared through https://github.com/821code/DialGNN.

The Violating Grice’s Maxim in the Dialogue Text of Student's English Book (Under the Merdeka Curriculum)

Penelitian ini menganalisis pelanggaran maksim prinsip kooperatif Grice. Penelitian ini menganalisis teks dialog buku bahasa Inggris siswa dan berada di bawah Kurikulum Merdeka. Peneliti berharap dapat memberikan penjelasan mengenai makna tersirat dari teks dialog dalam buku tersebut. Pertanyaan mengenai macam-macam maksim dalam Prinsip Kerja Sama yang perlu dipatuhi, dan bagaimana implikatur tersebut muncul dalam teks dialog. Dengan melakukan hal ini peneliti mampu memberikan penjelasan tentang makna tersirat dari teks dialog dalam buku tersebut. Tujuan dari penelitian ini adalah memberikan pemahaman bahwa guru perlu menjelaskan sesuatu di luar bahasa. Buku siswa memandu mereka dalam menggunakan bahasa target. Analisis ini termasuk metode deskriptif-kualitatif. Hasil analisis menunjukkan beberapa maksim mengambang dalam dialog; ada maksim kuantitas &amp; relevansi yang mengambang. Pelanggaran maksim kuantitas berjumlah dua dan maksim relevansi berjumlah satu. Teks dialog dalam buku harus memberikan siswa pemahaman yang baik tentang bagaimana menggunakan bahasa tersebut. Inklusi, penting bagi semua pemangku kepentingan yang peduli terhadap hal ini. Pendidik hendaknya memberikan isi dalam buku siswa sebagaimana adanya dalam pemahaman holistik

Penggunaan Metode Audiolingual dalam Maharah Istima' di MTs. KH. Hasyim Asy’ari Malang

The research conducted by this researcher aims to find out 1) The use of the audiolingual method in maharah istima' at MTs. KH. Hasyim Asy'ari Malang 2) The steps of using the audiolingual method 3) The technique of using the audiolingual method 4) The advantages and disadvantages of the audiolingual method. The method used in this research is qualitative with research instruments in the form of interviews, observations, and documentation. As a result, 1) the use of the audiolingual method for listening skill aims so that students are able to understand speaker's speech correctly and well without feeling difficult 2) The steps for using the Audiolingual Method include presenting dialogue texts, students memorizing dialogues, presenting sentence patterns in dialogue, dramatize the dialogue, and the teacher asks students to compose sentences according to the sentence patterns in the dialogue 3) The technique used in the use of this method is listening to dialogue, memorizing dialogue, dictation, and connecting words 4) The advantage of this method is that students become proficient in forming patterns. sentences, have good and correct pronunciation, be active in dialogue, gain direct language experience while the drawback is that students tend to copy or imitate because they pay less attention to when learning takes place

Multi-modal fusion for business process prediction in call center scenarios

Call centers are critical for gathering customer feedback, making them essential for business communication. Predicting the ongoing business process status accurately has become a focus in both academia and industry. However, current methods mainly analyze process sequence data from enterprise information systems, missing out on valuable data from other sources like call centers. Moreover, these methods often focus on a single task, ignoring the shared information across multiple tasks. This paper presents a novel method for business process prediction that fuses multi-modal data from both information systems and call centers. Specifically, the method combines sequence data from the enterprise information system and dialogue text data from the call center for a more enriched business process prediction. Additionally, to navigate the multi-task learning conundrum, we improve the existing MMoE algorithm and introduce a new multi-task learning architecture called Heterogeneous Multi-gate Mixture-of-experts. The experimental results over some current approaches like Transformer, CNN and LSTM show superior prediction performance compared to baseline models, demonstrating that our method can help call centers optimize their processes, improve customer service, and drive business success.

Design of A Profile of Needs for Basic Reading Teaching Materials in Riau Province

This research aims to design a profile of needs for basic reading teaching materials, especially at the tertiary level. This study used a descriptive method with a qualitative approach. Research data was collected using a questionnaire. The profile questionnaire for the need for teaching materials for the basics of reading was displayed on the Google form platform. The research data was in the form of questionnaire results distributed based on the needs of students in the Indonesian Language and Literature Education Study Program which was available at 3 leading universities in Riau Province, namely Riau Islamic University, Riau University and Sultan Syarif Kasim Riau State Islamic University. After getting the questionnaire results, data validation was carried out using interviews. Next, the data analysis technique was carried out using reflective techniques with the help of TagCrowd. The results of the research showed that the profile of the need for teaching materials for the basics of reading required by Language and Literature Education Students in Riau Province was based on four factors, namely factors (1) the content needs of students who want to understand the theory of reading skills, of course in accordance with real life and mastery of skills, (2) ) students' linguistic needs want popular vocabulary with complex information using simple sentences to make it easier to understand, (3) students' graphic needs want a book display with Times News Roman letters, 12 and A4 size paper equipped with pictures, audio and video, 21x29 cm using frames, and (4) students' learning media needs require teaching media in the form of printed books/e-books with interactive material containing public facilities such as tourist attractions and entertainment.) in the form of dialogue texts.

Feature Extraction of Dialogue Text Based on Big Data and Machine Learning

In this article, a dialogue text feature extraction model based on big data and machine learning is constructed, which transforms the high-dimensional space of text features into the low-dimensional space that is easy to process, so that the best feature words can be selected to represent the document set. Tests show that in most cases, the classification accuracy of this model is higher than 88%, and the recall rate is higher than 85%, thus achieving the goal of higher classification accuracy with less computation. When extracting the features of dialogue texts, there is no need for preprocessing, just count the data such as lexical composition, sentence length and sentence-to-sentence relationship of the target text, and make linear analysis to obtain key indicators and weights. Based on this, the classification model can achieve good results, thus effectively reducing the workload and computation of text classification.

Human-computer interaction: Evaluation of trust and emotional

With the advancement of information technology, human-computer interaction is becoming increasingly intelligent. This progress has led to the transformation of the car cabin from a simple driving space to an intelligent mobile life terminal. At the same time, more and more people have psychological problems in modern society, and timely adjustment of their negative emotions is of great practical significance to social harmony and stability. Traditional methods of emotion regulation require a lot of manpower and energy. Hence, this paper presents an approach that utilizes text dialogue and somatosensory interaction to detect the users emotions and subsequently adjust them. By examining and analyzing the current research landscape, the objective of this study is to offer insights for designing trustworthy and emotionally-responsive interactions in the automotive context. It can bring a better driving experience to smart car users and bring benefits to many companies. It is of great scientific and application value to carry out this research in depth to promote the development of human-computer interaction. Through literature review, case study, theoretical analysis and other research methods. This paper sorts out the establishment of trust degree and recognition of emotional changes in human-computer interaction, and puts forward the role, defects and improvements of trust establishment in human-car cooperation, as well as the reasons affecting user emotions, and how to improve the defects of the application of voice emotion recognition. It also discusses difficulties in establishing trust and misperception of emotions.