Language Utterances Research Articles

A comparative study of how teachers communicate in deaf education classrooms.

This study investigates the communication practices of four teachers in 3rd to 6th grade classrooms with 9 deaf students with limited language proficiency and in stages of emergent writing development. Analyzing language modalities, utterance types, and class interactivity, we found that teachers using American sign language used student-centered approaches, generating a greater number of directives and responsive utterances. They persevered in increasing students' engagement and were successful in clarifying misunderstandings. Teachers using spoken English used teacher-centered approaches, making general comments directed at the whole class, which consequently reduced student participation and responsiveness. They also largely avoided repairing communication breakdowns with emergent writers, focusing instead on those with greater auditory and speaking abilities. These patterns reveal disparities in classroom communication that can affect student learning. Our findings highlight the need for teacher preparation programs to equip teachers with skill sets to employ accessible and effective communication during instruction, especially with deaf students who are still developing foundational language and writing skills.

NoviCode: Generating Programs from Natural Language Utterances by Novices

Abstract Current Text-to-Code models demonstrate impressive capabilities in generating executable code from natural language snippets. However, current studies focus on technical instructions and programmer-oriented language, and it is an open question whether these models can effectively translate natural language descriptions given by non-technical users and express complex goals, to an executable program that contains an intricate flow—composed of API access and control structures as loops, conditions, and sequences. To unlock the challenge of generating a complete program from a plain non-technical description we present NoviCode, a novel NL Programming task, which takes as input an API and a natural language description by a novice non-programmer, and provides an executable program as output. To assess the efficacy of models on this task, we provide a novel benchmark accompanied by test suites wherein the generated program code is assessed not according to their form, but according to their functional execution. Our experiments show that, first, NoviCode is indeed a challenging task in the code synthesis domain, and that generating complex code from non-technical instructions goes beyond the current Text-to-Code paradigm. Second, we show that a novel approach wherein we align the NL utterances with the compositional hierarchical structure of the code, greatly enhances the performance of LLMs on this task, compared with the end-to-end Text-to-Code counterparts.

Techniques for Detecting the Start and End Points of Sign Language Utterances to Enhance Recognition Performance in Mobile Environments

Recent AI-based technologies in mobile environments have enabled sign language recognition, allowing deaf individuals to communicate effectively with hearing individuals. However, varying computational performance across different mobile devices can result in differences in the number of image frames extracted in real time during sign language utterances. The number of extracted frames is a critical factor influencing the accuracy of sign language recognition models. If the number of extracted frames is too small, the performance of the sign language recognition model may decline. Additionally, detecting the start and end points of sign language utterances is crucial for improving recognition accuracy, as the period before the start point and after the end point often involves no action being performed. These parts do not capture the unique characteristics of each sign language. Therefore, this paper proposes a technique to dynamically adjust the sampling rate based on the number of frames extracted in real time during sign language utterances in mobile environments, with the aim of accurately detecting the start and end points of the sign language. Experiments were conducted to compare the proposed technique with the fixed sampling rate method and with the no-sampling method as a baseline. Our findings show that the proposed dynamic sampling rate adjustment method improves performance by up to 83.64% in top-5 accuracy and by up to 66.54% in top-1 accuracy compared to the fixed sampling rate method. The performance evaluation results underscore the effectiveness of our dynamic sampling rate adjustment approach in enhancing the accuracy and robustness of sign language recognition systems across different operational conditions.

Dialek Sosial Masyarakat Bugis-Makassar Kelurahan Minasa Upa Kecamatan Rappocini Kota Makassar

Language and society are very closely related, this relationship includes age and social status which mutually influence language, voice color and accent for each language speaker. Sociolect (from the words social and dialect) or social dialect is a language variation associated with a particular social group. Sociolect occurs in various groups of society according to social class, age and occupation. Language differences between upper class and lower class society, teenagers and old people, doctors and lawyers, are examples of social dialect. This research aims to describe the use of sociolectic language variations in communication between the Bugis-Makassar community, Minasa Upa Village, Rappocini District, Makassar City. The data in this research are all everyday language utterances in communication that are identified as using sociolect. Meanwhile, the data source used is the Bugis-Makassar community who live in Minasa Upa Village, Rappocini District, Makassar City. Data collection techniques include; observation techniques, recording techniques and note-taking techniques. The data analysis technique is carried out in an interpretative descriptive manner, namely data that has been collected on a device, listened to and recorded, then identified and analyzed in depth by sorting and selecting information according to the research focus, namely the various forms of sociolect in communication among the Bugis-Makassar community. Based on the results of the research, the researchers found 16 data on sociolectic language variations, especially conversations in terms of age level, nobility level, and education level, with details of 8 data variations of colloquial language, 2 data variations of slang language, and 6 data variations of vulgar language.

SPATIAL DEIXIS IN BIMANESE AND ENGLISH: AN INVESTIGATION SIMILATIES AND DIFFERENCES

Spatial deixis is the language utterance that is employed by the speaker to convey the location of objects or events in space. The purpose of this research is to look into the aspects of specific deixis that exist in both Bimanese and English, as well as how individuals use them in natural conversation. This study next looks at the parallels and differences between spatial deixis in Bimanese and English. To collect data on the use of spatial deixis in Bimanese, the study used qualitative research methods such as semi-structured interviews and direct observation. In the meantime, documentation such as books, articles, or movies is employed to examine particular deixes in English. The subjects of this research were drawn from five distinct locations in Bima regency: Pela, Wawo Rada, Bima City, Wera, and Sila. The data was then descriptively evaluated by identifying, categorizing, describing, and explaining related to the people employing specific deixis in Bimanese. This study found that the aspects of spatial deixis in Bimanese can be seen from the deictic system namely; locative adverbs are marked by ta (ta ake, ta ede, and, ta aka), demonstrative pronoun (ake, ede, and aka), topography system (ese, awa, dei ari, and ipa), four corners direction (ɗa, ɗo, ɗi, ele), and political dimension (ese and awa). In English, there are various types of spatial deixis: demonstrative pronouns (this, that, and with plural forms), locative adverbs (here and there), and topography systems (up and down). The three aspects of spatial deixis that are similar between English and Bimanese are demonstrative pronouns (proximal and distal), locative adverbs (proximal and distal), and topographical system (up and down). Although the 'medial' concept in Bimanese has additional form in terms of demonstrative and locative adverbs. While there are distinctions between them, the Bimanese has many more spatial deixis aspects than English in terms of directions, topography systems, and socio-political dimensions.

Building a Turkish UCCA dataset

Abstract Semantic representation is the task of conveying the meaning of a natural language utterance by converting it to a logical form that can be processed and understood by machines. It is utilised by many applications in natural language processing (NLP), particularly in tasks relevant to natural language understanding (NLU). Due to the widespread use of semantic parsing in NLP, many semantic representation schemes with different forms have been proposed; Universal Conceptual Cognitive Annotation (UCCA) is one of them. UCCA is a cross-lingual semantic annotation framework that allows easy annotation without requiring substantial linguistic knowledge. UCCA-annotated datasets have been released so far for English, French, German, Russian, and Hebrew. In this paper, we present a UCCA-annotated Turkish dataset of 400 sentences that are obtained from the METU-Sabanci Turkish Treebank. We provide the UCCA annotation specifications defined for the Turkish language so that it can be extended further. We followed a semi-automatic annotation approach, where an external semantic parser is utilised for the initial annotation of the dataset, which is manually revised by two annotators. We used the same semantic parser model to evaluate the dataset with zero-shot and few-shot learning, demonstrating that even a small sample set from the target language in the training data has a notable impact on the performance of the parser (15.6% and 2.5% gain over zero-shot for labelled and unlabelled results, respectively).

Intent detection for task‐oriented conversational agents: A comparative study of recurrent neural networks and transformer models

AbstractConversational assistants (CAs) and Task‐oriented ones, in particular, are designed to interact with users in a natural language manner, assisting them in completing specific tasks or providing relevant information. These systems employ advanced natural language understanding (NLU) and dialogue management techniques to comprehend user inputs, infer their intentions, and generate appropriate responses or actions. Over time, the CAs have gradually diversified to today touch various fields such as e‐commerce, healthcare, tourism, fashion, travel, and many other sectors. NLU is fundamental in the natural language processing (NLP) field. Identifying user intents from natural language utterances is a sub‐task of NLU that is crucial for conversational systems. The diversity in user utterances makes intent detection (ID) even a challenging problem. Recently, with the emergence of Deep Neural Networks. New State of the Art (SOA) results have been achieved for different NLP tasks. Recurrent neural networks (RNNs) and Transformer architectures are two major players in those improvements. RNNs have significantly contributed to sequence modelling across various application areas. Conversely, Transformer models represent a newer architecture leveraging attention mechanisms, extensive training data sets, and computational power. This review paper begins with a detailed exploration of RNN and Transformer models. Subsequently, it conducts a comparative analysis of their performance in intent recognition for Task‐oriented (CAs). Finally, it concludes by addressing the main challenges and outlining future research directions.

Are there fractals in sign language?

Sign language is analyzed from the perspective of Menzerath-Altmann law (MAL). In reply to a question posed in the title, this study involves a comprehensive fractal analysis of a concrete Czech sign language utterance at multiple linguistic levels. Through this examination, a novel linguistic level, the so-called “pseudosyllables”, is unearthed and subjected to thorough investigation. While the results of this experimental exploration do not conclusively confirm fractal structure in sign language overall via an isomorphism between the fractal dimension formula and MAL, we undeniably detect the existence of pseudofractal objects of sign language in the sense of the modified (whence the prefix “pseudo”) conjecture by Hřebíček on the basis of our arguments, particularly linked to this newly identified linguistic level.

Implementation of Learn to Play Therapy for Children in Special Schools.

Play is the primary occupation in childhood and fundamental to occupational therapy practice. To evaluate a play intervention in special school settings. Pre- and postinvolvement of a 7-mo play program. Four special schools in Victoria, Australia, for children with IQs < 70. Thirty-eight children with diagnoses including intellectual disability, autism, and global developmental delay, 7 teachers, 2 speech pathologists, and 2 occupational therapists. Learn to Play Therapy for 1 hr per week over a 7-mo period. Pre-post outcome measures included children's pretend play skills, language, social skills, emotional regulation, and academic competence. Mean age of 38 children (15 girls and 23 boys) at baseline was 5 yr 7 mo (SD = 0.46 yr). Results showed significant changes in children's pretend play (p = .03), ability to recall sentences (p = .02), social skills (p = .022), and academic competence (p = .012). Learn to Play had a large effect on children's narrative skills (d = 2.72). At follow-up, object substitution at baseline influenced expressive language (p < .001), narrative mean language utterance (MLU; p = .015), social skills (p < .001), and academic competence (p < .001); elaborate play at baseline plus time influenced social skills (p < .001); and elaborate play at baseline influenced narrative MLU (p =. 016), sentence recall (p = .009), and academic competence (p = .001). Embedding pretend play within practice positively influenced children's language, narrative, social, and academic skills. Plain-Language Summary: This study adds to the limited research on play-based therapy programs in special school settings for children with an IQ of less than 70. Children participated in Learn to Play Therapy, during which an occupational therapist, who has observed and assessed the child's play and understands the child's play abilities, played beside the child. Learn to Play Therapy is a child-centered therapy that is used to increase a child's ability to self-initiate and enjoy pretend play. The positive impacts of supporting the children's pretend play ability were highlighted by increases in their pretend play, language, social skills, academic competence, and narrative language after participating in Learn to Play Therapy in their special schools.

Rational Value in the Identity Political Discourse of 2024 RI Presidential Candidates

In politics, discourse plays an important role as a stage for political actors to utilize language as a tool to build, reveal, or strengthen their power domination. Political discourse is a crucial element of political communication, providing a platform for political actors to utilize language to establish, reveal, or reinforce power dynamics. Repetition is a common feature in political discourse, often used by politicians to emphasize key messages and concepts. This research aims to identify and explore the various dimensions of identity politics, including social, ethnic, religious, professional, regional, gender, and cultural identities. This research will use the critical discourse analysis (AWK) method to process and analyze data. The type of research conducted is descriptive qualitative. The data to be analyzed are the campaign speeches of RI 2024 presidential candidates delivered in the form of language utterances, especially focusing on grammatical relational values that reflect identity politics, obtained from YouTube social media. The data source comes from the video of the campaign speech of the 2024 RI presidential candidate uploaded on the Kompastv account on YouTube. The data collection techniques to be used include documentation, listening technique, and note-taking technique. Through the use of command sentences, the speaker gives instructions to his supporters to perform certain actions and aims to influence the listeners' behavior and actions. Meanwhile, the use of statement sentences reinforces a clear and unequivocal message regarding the social relationship between the presidential candidate and his supporters, emphasizing commitment and continuity in the relationship. In addition, the use of relational modality creates an atmosphere of hope and belief in the positive outcome of the struggle and the desired change, strengthening the emotional attachment between the speaker and his supporters.

Translation techniques of the positive politeness utterances in the Pay It Forward movie

This descriptive qualitative research employed the methods of observation and auto-expert judgment for the data collection and applied Brown &amp; Levinson’s politeness theory (1987) and Molina &amp; Albir’s translation theory (2002) for the data analysis. This research focused on finding the positive politeness strategies and the translation techniques of the utterances obtained from Pay It Forward movie as the data source. Based on the findings of the research, there were 40 utterances with positive politeness strategies as the data under analysis. The research findings, among others, showed that the strategies of Intensify interest to hearer (17.50%) and Exaggerate (17.50%) were dominantly used in the Pay It Forward movie, while the translation strategy of Established equivalent (40.00%) was mostly used in the translation of positive politeness utterances in the movie. It can be concluded here that the translator translates the positive politeness utterances of the source language (English) into the target language (Indonesian) appropriately through the Established equivalent strategy without making any significant changes to maintain the meaning relatively the same in the Indonesian translation subtitles.

‘Men More Polite Than Women?': Gender Differences Study of Bimanese Interaction in Requesting

This study examines gender differences in request speech and politeness, focusing on how men and women use different linguistic strategies to convey requests politely. This study aims to analyze the alerter and supporting speech elements in request speech in the Bima language as spoken by male and female students. It also compares the request speech elements used by men and women. Using a qualitative approach, the data analyzed consists of request utterances in the Bima language. Data were collected through recording techniques and discourse completion tests. The data analysis involved categorizing the speech elements as units of analysis, focusing on alerter and supporting elements, and classifying them based on Blum-Kulka’s framework. The analysis revealed that male speech tended to demonstrate familiarity with the interlocutor through the varied use of alerter greetings, while female speech showed politeness by employing supporting speech. This difference indicates that men are more inclined to use positive politeness strategies, whereas women tend to use negative politeness strategies.

The moderating role of L2 proficiency in the predictive power of L1 fluency on L2 utterance fluency

The current study examined the extent to which first language (L1) utterance fluency measures can predict second language (L2) fluency and how L2 proficiency moderates the relationship between L1 and L2 fluency. A total of 104 Japanese-speaking learners of English completed different argumentative speech tasks in their L1 and L2. Their speaking performance was analysed using measures of speed, breakdown, and repair fluency. L2 proficiency was operationalised as cognitive fluency. Two factor scores of cognitive fluency—linguistic resources and processing speed—were computed based on performance in a set of linguistic knowledge tests capturing vocabulary knowledge, morphosyntactic processing, and articulatory skills. A series of generalised linear mixed-effects models revealed small-to-moderate effect sizes for the predictive power of L1 utterance fluency measures on their L2 counterparts. Moderator effects of L2 proficiency were found only in speed fluency measures. The relationship between L1 and L2 speed fluency was weaker for L2 learners with wider L2 linguistic resources. Conversely, for those with faster L2 processing speed, the L1-L2 link tended to be stronger. These findings indicate that the L1-L2 fluency link is subject to the complex interplay of phonological differences between learners’ L1 and L2 and their L2 proficiency, offering implications for diagnostic speaking assessment.

A semi-supervised learning approach for semantic parsing boosted by BERT word embedding

In the modern world, structured and semi-structured knowledge bases hold a considerable amount of data. There-fore, people who are familiar with formal query languages should not be the only ones who can efficiently and clearly query them. Semantic Parsing (SP) is converting natural language utterances into formal meaning representations. The paper suggests a model for SP that uses a novel method of utilizing the Semi-Supervised Generative Adversarial Network (SS-GAN) to enhance the classifier performance. The proposed SS-GAN extends the fine-tuning of word embedding architectures using unlabeled examples in a generative adversarial environment. We provide a regularization strategy for addressing the mode missing problem and unstable training in SS-GAN. The main viewpoint is to use the extracted feature vectors from the discriminator. Hence, the generator produces outputs by aiding the discriminator’s learned features. A reconstruction loss is added to the loss function of the SS-GAN to drive the genera-tor to reconstruct outputs from the discriminator’s features, hence steering the generator toward actual data configurations. The proposed reconstruction loss improves the performance of SS-GAN, produces high-quality outputs, and may be combined with other regularization loss functions to improve the performance of diverse GANs. We employ BERT word embedding for our model, which can be included in a downstream task and fine-tuned as a model, while the pre-trained BERT model can capture various linguistic properties. We examine the suggested model using the WikiSQL and SparC datasets, and the analysis findings reveal our model outperforms its rivals. The findings from our experiments indicate that the need for labeled samples can be minimized, down to as few as 100 instances, while still achieving commendable classification outcomes.

An Analysis of Code Switching Found in Novel “The Paragon Plan”

Bilingualism is native-like control of two languages. Has even suggested that Bilingualism commences when a person begins to understand utterances in a second language. Code-switching frequently occurs if the speakers can speak more than one language and use the languages simultaneously in the same context. This means the speakers intend to keep the utterance's meaning the same when emphasizing the purpose. In this research, the data was collected by using the observation method. The data of this study were taken from The Paragon Plan novel by Aranindy. The data was analyzed based on a theory proposed by Poplack (1980) to find out and categorize the types of code-switching. Meanwhile, the reasons for code-switching were analyzed by using the theory by Hoffman (1991). In analyzing the data, descriptive and qualitative methods were used. The types of inter-sentential switching are the most dominant in the novel The Paragon Plan with 30 data or 44.8%, followed by inter-sentential switching with a total of 24 data or 35.8%, and followed by tag switching amounted to 13 data or 19.4% of 67 data. Concerning the seven reasons for code switching, the findings indicate that only two were identified in the novel “The Paragon Plan”. Talking about particular topic is the most dominant reasons of code switching. Ultimately, code-switching in a novel can serve various narratives, character-building, and stylistic purposes. It enriches the story's linguistic and cultural landscape, making it more engaging and relatable for readers.

Longitudinal development of second language utterance fluency, cognitive fluency, and their relationship

AbstractThe development of L2 utterance fluency has been extensively researched, whereas that of cognitive fluency has rarely been examined. This study investigated the longitudinal development of L2 utterance and cognitive fluency and their relationship. Thirty-one Chinese learners of English completed speaking tasks and a set of tasks for cognitive fluency before and after 5 months’ study abroad. The results showed that participants made a significant improvement in mean syllable duration, end-clause pause frequency, and the speed of syntactic encoding and articulation but not in mid-clause pause frequency or lexical retrieval speed. Mixed-effects modeling confirmed a significant relationship between syntactic encoding speed and mean syllable duration and mid-clause pausing. Furthermore, the significant relationships were maintained over time. The findings highlight (a) the differences between mid-clause and end-clause pausing in terms of their developmental patterns and relationship with cognitive fluency and (b) a significant role of syntactic encoding speed in L2 utterance fluency.

ТАКСОНОМИЯ ПАРЕМИЙ В КИТАЙСКОМ ЯЗЫКОВЕДЕНИИ: СТРУКТУРНО-СЕМАНТИЧЕСКИЕ И ЛИНГВОПРАГМАТИЧЕСКИЕ АСПЕКТЫ

The article systematizes linguistic approaches to the classification of Chinese language utterances and ready-made expressions. Stable expressions, known in Chinese linguistics as shuyu, are an integral part of language and culture. Based on the analysis of theoretical works on Chinese paremiology, as well as the author's observation of linguistic facts, the formal-structural and speech features of several varieties of shuyu (yanyu, suyu, sehoyu, chengyu, guanyungyu) are considered, the linguapragmatic properties of each of these types of speech expressions are described, and specific examples are considered. The results of the study contribute to the modern theory of Chinese paremiology, and will also be useful as a theoretical source for comparative studies of Chinese and Russian paremias.

Assessing the utility of text-to-SQL approaches for satisfying software developer information needs

Software analytics integrated with complex databases can deliver project intelligence into the hands of software engineering (SE) experts for satisfying their information needs. A new and promising machine learning technique known as text-to-SQL automatically extracts information for users of complex databases without the need to fully understand the database structure nor the accompanying query language. Users pose their request as so-called natural language utterance, i.e., question. Our goal was evaluating the performance and applicability of text-to-SQL approaches on data derived from tools typically used in the workflow of software engineers for satisfying their information needs. We carefully selected and discussed five seminal as well as state-of-the-art text-to-SQL approaches and conducted a comparative assessment using the large-scale, cross-domain Spider dataset and the SE domain-specific SEOSS-Queries dataset. Furthermore, we study via a survey how SE professionals perform in satisfying their information needs and how they perceive text-to-SQL approaches. For the best performing approach, we observe a high accuracy of 94% in query prediction when training specifically on SE data. This accuracy is almost independent of the query’s complexity. At the same time, we observe that SE professionals have substantial deficits in satisfying their information needs directly via SQL queries. Furthermore, SE professionals are open for utilizing text-to-SQL approaches in their daily work, considering them less time-consuming and helpful. We conclude that state-of-the-art text-to-SQL approaches are applicable in SE practice for day-to-day information needs.

Lip Synchrony of Bilabial Consonants in the Lithuanian Dubbed Live-Action Filma Dog's Way Home

AbstractThis article explores the intricate nature of lip synchrony in dubbing as an audiovisual translation mode, highlighting its significance beyond aesthetic considerations. Often overlooked during the translation process for dubbing, the mismatch between auditory and visual signals caused by unsynchronized lip movements can have a negative impact on speech perception. Moreover, with the constant rise of audiovisual content, achieving satisfactory lip synchrony remains a crucial challenge that demands attention. Although scholars such as Fodor (1976) and Chaume (2012) have recognized the importance of bilabial consonants in maintaining lip synchrony, there is a notable research gap specifically focusing on this aspect within the Lithuanian dubbed industry. To address this gap, this study investigates the lip synchrony of bilabial consonants in the Lithuanian dubbed version of the live-action filmA Dog's Way Home(2019). By employing a comparative research approach that integrates qualitative and quantitative analyses, the study draws on theoretical perspectives presented by McGurk and MacDonald (1976), Fodor (1976), Chaume (2004, 2012), and Koverienė (2015). The analysis of the cinematographic shots reveals that only a small percentage (19%) of the bilabial phonemes in the source language utterances were visually prominent instances, and consequently chosen for detailed examination of lip synchrony. This finding suggests that strict adherence to lip synchrony may not be crucial in numerous instances, allowing translators to have greater flexibility in their approach. The target language utterances demonstrate a relatively high percentage (approximately 76%) of synchronous cases for bilabials, particularly in proper names and international words. However, the research also reveals additional 94 instances of bilabials in the target language that were not found in the source language utterances, leading to dischronemes. Therefore, audiovisual translators are urged to consider not only how to maintain the lip synchrony of bilabial consonants in the target language, but also how to avoid the emergence of new bilabials.

Though this be hesitant, yet there is method in ’t: Effects of disfluency patterns in neural speech synthesis for cultural heritage presentations

This study presents the results of two perception experiments aimed at evaluating the effect that specific patterns of disfluencies have on people listening to synthetic speech. We consider the particular case of Cultural Heritage presentations and propose a linguistic model to support the positioning of disfluencies throughout the utterances in the Italian language. A state-of-the-art speech synthesizer, based on Deep Neural Networks, is used to prepare a set of experimental stimuli and two different experiments are presented to provide both subjective evaluations and behavioural assessments from human subjects. Results show that synthetic utterances including disfluencies, predicted by a linguistic model, are identified as more natural and that the presence of disfluencies benefits the listeners’ recall of the provided information.