Dialogue Sentences Research Articles

Metode dan Strategi Penerjemahan Bahasa Arab ke Bahasa Indonesia pada Subtitle Film “Ar Risalah”

This study discusses the application of translation methods and strategies in the subtitles of the film Ar Risalah from Arabic to Indonesian. The film Ar Risalah is a religious film that tells the story of the life of the Prophet Muhammad SAW, which has strong historical and cultural values. This study aims to identify the methods and strategies used in translating dialogue sentences in the film's subtitles. Using a qualitative descriptive research method, data were collected through direct observation of the film's dialogue. The researcher classified the translation methods based on Newmark's theory and translation strategies from Pamungkas and Akmaliyah. The results of the study showed that there were seven translation methods used, namely word-for-word, literal, faithful, semantic, adaptation, free, and idiomatic translation. In addition, there were four translation strategies, namely prioritizing and ending, reducing, adding, and replacing. These findings indicate that translators must consider lexical, contextual, and cultural aspects in producing translations that are communicative and in accordance with the target language. This study provides in-depth insight into the complexity of the film translation process, which is expected to be a reference for subtitle translators in the future.

Easy and effective! Data augmentation for knowledge-aware dialogue generation via multi-perspective sentences interaction

In recent years, knowledge-based dialogue generation has garnered significant attention due to its capacity to produce informative and coherent responses through the integration of external knowledge into models. However, obtaining high-quality knowledge that aligns with the dialogue content poses a considerable challenge, necessitating substantial time and resources. To tackle the issue of limited dialogue data, a majority of research endeavors concentrate on data augmentation to augment the volume of training data. Regrettably, these methods overlook knowledge augmentation, leading to a restricted diversity in input data and yielding enhancements solely in specific metrics. Real-world conversations exhibit a spectrum of characteristics, including repetitions, reversals, and interruptions, demanding a heightened level of data diversity. In this study, we introduce a straightforward yet effective data augmentation technique known as Multi-perspective Sentence Interaction to bolster the connections among sentences from varied viewpoints. Through an examination of target responses from multiple dialogue perspectives, we enhance our comprehension of the relationships between dialogue sentences, thereby facilitating the expansion of knowledge-based dialogue data. Through experiments conducted on various knowledge-based dialogue datasets and utilizing different models, our findings illustrate a notable enhancement in the quality of model generation facilitated by our method. Specifically, we observed a 3.5% enhancement in reply accuracy and a 0.1506 increase in diversity (DIST-2). Moreover, there was a substantial improvement in knowledge selection accuracy by 19.04% and a reduction in model perplexity by 31.48%.

THE DESCRIPTION OF CAKE APPLICATION ASSISTANCE TO THE STUDENTS’ ENGLISH SPEAKING SKILL AT SMA SWASTA ST XAVERIUS GUNUNGSITOLI

This study aims to describe the assistance of the cake application in learning a speaking skill and to describe the students’ reason for utilizing the cake application in learning the students' speaking skills. The research was conducted in class X at SMA Swasta Santu Xaverius Gunungsitoli with a total of 20 students. The research method used is a qualitative descriptive approach. Data were collected through observations and interviews. The results show that students use the cake application and can utilize the cake application in speaking skill. Students take advantage of it by studying the video menu, conversation dialogue, and path menu. The most frequently utilized by students is the conversation dialogue menu because this menu is very complete. Students can learn speaking, dialogue with native speakers, students can see errors the pronunciation in dialogue sentences, and students are also helped by explanations at the end of the dialogue and equipped with Indonesian translations. However, students also need to actively practice and interact with fellow students and native English speakers

Cross-modal knowledge distillation for continuous sign language recognition

Continuous Sign Language Recognition (CSLR) is a task which converts a sign language video into a gloss sequence. The existing deep learning based sign language recognition methods usually rely on large-scale training data and rich supervised information. However, current sign language datasets are limited, and they are only annotated at sentence-level rather than frame-level. Inadequate supervision of sign language data poses a serious challenge for sign language recognition, which may result in insufficient training of sign language recognition models. To address above problems, we propose a cross-modal knowledge distillation method for continuous sign language recognition, which contains two teacher models and one student model. One of the teacher models is the Sign2Text dialogue teacher model, which takes a sign language video and a dialogue sentence as input and outputs the sign language recognition result. The other teacher model is the Text2Gloss translation teacher model, which targets to translate a text sentence into a gloss sequence. Both teacher models can provide information-rich soft labels to assist the training of the student model, which is a general sign language recognition model. We conduct extensive experiments on multiple commonly used sign language datasets, i.e., PHOENIX 2014T, CSL-Daily and QSL, the results show that the proposed cross-modal knowledge distillation method can effectively improve the sign language recognition accuracy by transferring multi-modal information from teacher models to the student model. Code is available at https://github.com/glq-1992/cross-modal-knowledge-distillation_new.

An Adaptive Contextual Relation Model for Improving Response Generation

An Adaptive Contextual Relation Model for Improving Response Generation

BVA-Transformer: Image-text multimodal classification and dialogue model architecture based on Blip and visual attention mechanism

Multimodal tasks have become a hot research direction in recent years. The emergence of large-scale models has gradually elevated the extensive multimodal tasks, achieving remarkable achievements. However, when it comes to fusing multiple modalities for multimodal tasks, how to better integrate multimodal features is still a problem worth exploring. In tasks such as sentiment analysis targeting a wide range of social media content, the use of features derived solely from the [CLS] token may lead to insufficient information. This paper proposes BVA-Transformer model architecture for image-text multimodal classification and dialogue, which incorporates the EF-CaTrBERT method for feature fusion and introduces BLIP for the transformation of images to the textual space. This enables the fusion of images and text in the same information space, avoiding issues of information redundancy and conflict compared to traditional feature fusion methods. In addition, we proposed a Global Features Encoder (GFE) module based on visual attention in the BVA-Transformer, which can provide more global and targeted auxiliary features for the [CLS] token. This enables the model to utilize more feature information in classification tasks under this feature fusion method and dynamically select information to focus on. We also introduced the Trv structure from EVA-02 in the Decoder part of BVA-Transformer, investigating its impact on the model performance. Furthermore, we designed a three-stage training to further enhance the model’s performance. Experimental results demonstrate that BVA-Transformer achieves high-quality classification while generating dialogue sentences. Compared to existing multimodal classification models on our validation dataset, it exhibits excellent performance.

Minor Sentences in Selected Disney Children's Stories

This study is intended to investigate the use of minor sentences in Disney children’s stories. These sentences do not follow the regular clause structure patterns or the variants of those structures. Minor sentences are of two types: irregular sentences (henceforth IrrSs) and nonsentences (henceforth NonSs). The study aims at (i) investigating the most frequent subtypes of IrrSs and NonSs in Disney stories, and (ii) identifying the most frequent type of minor sentences in Disney children’s stories. Fulfilling the above aims, the study hypothesizes that independent phrase is the most frequent subtype of NonSs, while elliptical sentence in dialogue is the most frequent subtype of IrrSs. The study examines the use of IrrSs and NonSs in fifteen Disney children’s stories. The data are analyzed according to Quirk et al.'s (1985) model. The results show that IrrS appears (293) times while NonS occurs (711) times, with percentages of (29.18%) and (70.81%) respectively. It is found also that elliptical sentences in dialogue is the most frequent subtype of IrrSs with a frequency rate amounting to (126, 43%) and independent phrases is the most frequent subtype of NonSs that occurs (302) times with a percentage of (42.47%). The study concludes that NonS is the most frequent type of minor sentence. It is also concluded that elliptical sentences in dialogue and independent phrases are the most frequent subtypes of IrrSs and NonSs respectively.

KONFLIK BATIN PERAN UTAMA DALAM NASKAH DRAMA SAMPEK ENGTAY KARYA N. RIANTIARNO KAJIAN PSIKOLOGI SASTRA

This study aims to describe the inner conflict in the main role of Engtay, in the drama script Sampek Engtay by N. Riantiarno using Sigmund Freud's Theory. The method used is a qualitative descriptive research method. The data used in this study are dialogue sentences, monologues delivered by the main character in the drama, data collection techniques using library techniques and using corpus data tables. From the results of this study, there are several forms of inner conflict experienced by the main character, including pain, disappointment, confusion, anxiety, failure. These factors cause the main character's inner conflict with Raden Mas Darmaji as his father, Ray Retno as his mother and Sampek. There are three styles used by the main character in overcoming inner conflicts, namely the fox style, the mouse deer style, and the turtle style. Keywords: Main Character, Inner Conflict, Literary Psychology, Drama, Sampek Engtay

Linguistic Features of Clients and Counselors for Early Detection of Mental Health Issues in Online Text-based Counseling.

Online counseling is essential for overcoming mobility restrictions, schedule limitations, and mental health stigma. However, government counseling offices are being inun-dated with consultations for which non-mental health supports are targeted. Therefore, we aim to create a classification model that classifies whether the clients have mental health issues or other issues. We expect to support counselors by presenting the classification results. We conducted the first automatic detection of clients who might be suffering from mental health issues and used almost 1000 actual counseling sessions for our machine learning framework. We achieved an F1-score of 0.646 by classifying dialogue sessions using features such as frequency-inverse, document frequency, document embedding of a large-scale language model, linguistic inquiry and word count, topic modeling, and statistics of dialogue sentences. In addition, we performed dimensionality reduction with principal component analysis. We also conducted evaluation experiments using dialogue sentences from the beginning to the middle of sessions as input and clarified the relationship between the number of messages in the dialogues and the transition in the classification performance. We also identified the words that contribute to detecting mental health issues for each client and counselor. Clinical relevance-This study makes it possible to detect the trends identified in a client's anxieties during counseling. Our findings are critical for designing systems that assist counselors.

A Study on Chinese Subtitle Translation Aspects of Conversation Implications in Korean Dramas - Focusing on the Drama “Misaeng”

A Study on Chinese Subtitle Translation Aspects of Conversation Implications in Korean Dramas - Focusing on the Drama “Misaeng”

Continuous-Time Attention for Sequential Learning

Attention mechanism is crucial for sequential learning where a wide range of applications have been successfully developed. This mechanism is basically trained to spotlight on the region of interest in hidden states of sequence data. Most of the attention methods compute the attention score through relating between a query and a sequence where the discrete-time state trajectory is represented. Such a discrete-time attention could not directly attend the continuous-time trajectory which is represented via neural differential equation (NDE) combined with recurrent neural network. This paper presents a new continuous-time attention method for sequential learning which is tightly integrated with NDE to construct an attentive continuous-time state machine. The continuous-time attention is performed at all times over the hidden states for different kinds of irregular time signals. The missing information in sequence data due to sampling loss, especially in presence of long sequence, can be seamlessly compensated and attended in learning representation. The experiments on irregular sequence samples from human activities, dialogue sentences and medical features show the merits of the proposed continuous-time attention for activity recognition, sentiment classification and mortality prediction, respectively.

TATHWIR MANHAJ TA'LIIM AL LUGAH AL 'ARABIYYAH AL ITTISOLIYYAT BI DAAR MUHIBBI AL 'ARABIYYAH AL JAAMI'AH AL ISLAMIYYAH AL HUKUUMIYYAH PEKALONGAN

The purpose of this research to examine the development ofcommunicative curriculum conducted by arabic institutions Dar MuhibAl-‘Arobiyah. The data were collected through correspondence anddocumentation. The method of this research is qualitative, the data analysisuses data triangulation, that is: data reduction, data presentation, andconclusion. The primary data sources in this study were teachers, students,and curriculum documents. The result of this research can be concluded thatthe development of communicative arabic language Dar Muhibbi al ’Arabiyyahstated not effective. It cause the methods, media, and evaluations used thatare used not relevant to the communicative objectives that have been decided.The Methods, media, and evaluations are more directed to memorizing manyvocabulary thah the practice of using sentences in dialogue. Suggestions: 1)The communicative learning focused on the process of producing senteceswith multiplying conversations; 2)The asatidz/teachers to apply the processof developing curriculum by existing frameworks example, Hilda Tabamodel.

The Use of Newmark’s Translation Methods in Translating Forman’s Novel 'I Was Here' from English into Indonesian

This research is aimed to find out and describe the use of Newmark’s translation methods in translating Gayle Forman’s novel I Was Here from English into Indonesian. Qualitative approach is applied because the data of this research are in the form of words, phrases, and sentences. The data of this research are taken from Gayle Forman’s novel I Was Here and its Indonesian translation by Poppy D. Chusfani. There are 1629 dialogue sentences data taken from the novel. In this research, the researcher collects and analyzes the data through examining documents. The data are collected by marking the dialogue sentences in both novels and put them into a table of observation. In doing the analysis, the researcher compares the dialogue sentences in both novels. The analysis of the translation methods is based on Pieter Newmark’s classification of translation methods. The findings of the research show that there are seven out of eight translation methods proposed by Newmark used. They are word-for-word translation method, literal translation method, faithful translation method, semantic translation method, free translation method, idiomatic translation method, and communicative translation method. Based on the frequency of the translation method used in translating the dialogue sentences of the novel which is free translation method shows that the translator wants to make the content and language of the translated text are acceptable and understandable for the target language (Indonesian) readers.
 Keywords: dialogue sentences, I Was Here, novel, translation methods

Речевое взаимодействие в триадах «мать—дизиготные близнецы» в 4—6-летнем возрасте детей

The longitudinal study of mother speech interaction with 4-6-years-old dizygotic twins (n=5 triads) was performed. The characteristics of maternal speech addressed to twins, characteristics of children’ speech development due to the order of the birth are investigated. The first-born child in pair have a higher level of speech development as compared to the second-born child by syntactic structure of sentences in dialogue, number of consonants, meanings of pitch in vowels. Correlation between characteristics of maternal speech and the level of twin’s speech development is revealed. When mothers interact with the second-born twins they stimulate them to communication more then the first-born twins.

The Neural Network Conversation Model enables the Commonly Asked Student Query Agents

One of the challenges in academic counselling is to provide an automated service system for students. There several query questions asking the faculty staffs about related-academic services each semester. Offered the communication interface more convenience, the novel approach based on neural network model is introduced to investigate the automated conversational agent. The pre-defined dialogue sentences were collected manually from the student query questions and used as the training dataset. The questions have been varied and grouped by topic-categorizing queried from the registration help desk of the department. Artificial intelligence and machine learning have contributed each other to build the conversational agent so-call KUSE-ChatBOT plugged and used in the modern messenger application, LINE. The system is also included the dialogue back-end management system to use in further deep learning model updating. Tensorflow, the machine learning development platform originated by Google, was performed and obtained the learning model using Python development kits. The LINE Messaging APIs is then contributed as the user interface where users could have FAQs' conversation via the LINE application. The KUSE-ChatBOT is outperformed and efficient by providing automated consultation to the students precisely with the accuracy rate over 75 percent. The system could assist the staffs to be able to lessen the workload of answering the same question repeatedly and give response to the student timely.

深層学習を用いた対話文への「かわいさ」の付与

深層学習を用いた対話文への「かわいさ」の付与

In vitro molecular machine learning algorithm via symmetric internal loops of DNA

Programmable biomolecules, such as DNA strands, deoxyribozymes, and restriction enzymes, have been used to solve computational problems, construct large-scale logic circuits, and program simple molecular games. Although studies have shown the potential of molecular computing, the capability of computational learning with DNA molecules, i.e., molecular machine learning, has yet to be experimentally verified. Here, we present a novel molecular learning in vitro model in which symmetric internal loops of double-stranded DNA are exploited to measure the differences between training instances, thus enabling the molecules to learn from small errors. The model was evaluated on a data set of twenty dialogue sentences obtained from the television shows Friends and Prison Break. The wet DNA-computing experiments confirmed that the molecular learning machine was able to generalize the dialogue patterns of each show and successfully identify the show from which the sentences originated. The molecular machine learning model described here opens the way for solving machine learning problems in computer science and biology using in vitro molecular computing with the data encoded in DNA molecules.

Class-Dependent Modeling for Dialog Translation

This paper presents a technique for class-dependent decoding for statistical machine translation (SMT). The approach differs from previous methods of class-dependent translation in that the class-dependent forms of all models are integrated directly into the decoding process. We employ probabilistic mixture weights between models that can change dynamically on a sentence-by-sentence basis depending on the characteristics of the source sentence. The effectiveness of this approach is demonstrated by evaluating its performance on travel conversation data. We used this approach to tackle the translation of questions and declarative sentences using class-dependent models. To achieve this, our system integrated two sets of models specifically built to deal with sentences that fall into one of two classes of dialog sentence: questions and declarations, with a third set of models built with all of the data to handle the general case. The technique was thoroughly evaluated on data from 16 language pairs using 6 machine translation evaluation metrics. We found the results were corpus-dependent, but in most cases our system was able to improve translation performance, and for some languages the improvements were substantial.

Robust Dependency Parsing of Spontaneous Japanese Spoken Language

Spontaneously spoken Japanese includes a lot of grammatically ill-formed linguistic phenomena such as fillers, hesitations, inversions, and so on, which do not appear in written language. This paper proposes a novel method of robust dependency parsing using a large-scale spoken language corpus, and evaluates the availability and robustness of the method using spontaneously spoken dialogue sentences. By utilizing stochastic information about the appearance of ill-formed phenomena, the method can robustly parse spoken Japanese including fillers, inversions, or dependencies over utterance units. Experimental results reveal that the parsing accuracy reached 87.0%, and we confirmed that it is effective to utilize the location information of a bunsetsu, and the distance information between bunsetsus as stochastic information.

Conversation and sentence-hood

Based on Chao ’s insights on the sentence structures of Chinese, this article demonstrates the impact conversation exerts on Chinese syntax by analyzing the functions of the so-called minor sentences in dialogue. It is shown that a sentence is in fact a fossilized dialogue. It is suggested that it might be more profitable to adopt a top-down approach-i.e., working from conversation to sentence-in Chinese syntactic research.