Language Sentences Research Articles

Improving Neural Machine Translation in the Field of Electrical Engineering by Using Sentence Backbone Information

Due to the limited availability of corpora in the field of Electrical Engineering and the presence of numerous specialized terms, neural machine translation (NMT) performs poorly in translating the sentence backbone information when it is applied to corpora in the field of Electrical Engineering. In response to this issue, A method to improve NMT by using the sentence backbone information is proposed in this paper. In the proposed method, the source language sentences are used as the input of the Sentence Backbone Information Extraction Model to obtain the sentence backbone information, and then the sentence backbone information are incorporated as an auxiliary during the training process of the NMT model. Furthermore, a module called the Sentence Backbone Information Enhancement Module is introduced. It utilizes the dependency parse trees of the source language sentences to generate the sentence backbone mask matrices. These matrices are then applied to the encoder to force the NMT model to pay more attention to the backbones of sentences. On the English-Chinese parallel corpus in the field of Electrical Engineering, the proposed method in this paper outperforms the Transformer baseline translation model by 1.25 BLEU points. And it outperforms the baseline model in both METEOR and ROUGE-L evaluation metrics. It indicates that the proposed method in this paper can effectively improve translation performance.

The Use of Large Language Models in Combination with the Ontological Approach for the Synthesis of Natural Language Text

This research presented an approach based on the application of structured prompt instructions to large language models (LLM). Methodological foundations for using an ontology-driven system of structured prompts in interaction with ChatGPT were developed. The ChatGPT system allows expanding the knowledge base by obtaining new information from existing knowledge units based on a set of contexts. Thus, methods for generating meaningful responses in natural language were considered using a large language model using ontological approaches and natural language contexts. Using the proposed methodology, the OntoChatGPT system was developed, which effectively extracts entities from contexts, classifies them and generates appropriate responses. An experiment on the reverse synthesis of natural language sentences based on their ontological representation using large language models allows to clearly demonstrate the effectiveness of using the concept of large language models in dialogue systems. The study highlights the versatility of the methodology, emphasizing its applicability not only to ChatGPT but also to other chatbot systems based on LLMs, such as Google’s Bard utilizing the PaLM 2 LLM. The implementation of this technology is demonstrated using the Ukrainian language

Pola Kalimat Opini Gubernur dalam JawaPos untuk Komunikasikan Hari Guru Nasional

This research highlights the crucial role of governors as regional leaders in conveying messages and policies to the community, especially through opinions published in the mass media. The focus on the use of formal and informal language in the governor's communication, as well as the analysis of sentence patterns in the opinion of the Governor of East Java in the November 25, 2023 edition of Jawa Pos are the main objectives of this study. Content analysis methods are used to evaluate opinion texts, examine sentence types, sentence patterns, and formal and informal language usage. The results showed the dominance of the use of active voice in the governor's opinion, indicating awareness of the subject as the main actor in the message conveyed. Common sentence patterns such as KSPO and SPK are often used, but there are also variations of other patterns that provide a broader dimension in communication. There is also a unique blend of formal and informal language in the governor's text, creating a more personal atmosphere and connecting with the audience more thoroughly. However, the study has limitations, especially in the overall representation of the governor's communication style that may not include all communication strategies in different situations. In conclusion, the study reveals complexity and variation in language use and sentence patterns in governors' opinions.

The interplay of verbs and argument structure constructions in second language processing: roles of verb’s lexical properties and verb–construction association

Abstract While verbs and argument structure constructions are essential for deriving sentence meaning, their roles in sentence processing remains less known. To address this issue, the present study explored how a verb’s lexical properties and the strength of verb–construction associations influence second language (L2) sentence processing. In two self-paced reading experiments, Korean-speaking learners of English and native English speakers read English argument structure constructions containing verbs with varying lexical properties and association strength. In both Experiment 1 (involving the prepositional dative construction) and Experiment 2 (involving the caused-motion construction), only the L2-English learners, and not the native speakers, spent more time integrating the target constructions with verbs of weaker associations than those with stronger associations. Furthermore, the learners exhibited greater difficulty with verb–construction integration when the verbs were less frequent and less concrete; L2 proficiency did not modulate these effects. Our findings support the constructionist approach, suggesting that both verbs and verb–construction associations play an instrumental role in sentence comprehension.

How is Logical Analysis Viable?

The idea and the actual exercises of logical analysis as a philosophical method are at the heart of the emergence of analytic philosophy in the beginning of the 20th century. Although analytic philosophy is most commonly said to emerge with the critique devised by G. E. Moore and Bertrand Russell of the then-dominant idealist currents of philosophy in Britain, the central conception of analysis at work in this process is not the dominant traditional conception of analysis as decomposition of concepts, but a fairly new, distinctive conception which we find to be exemplified exclusively in Frege’s and Russell’s works (specifically Frege 1879, 1893 and Russell 1905). This distincive conception has been specified under a few various rubrics, such as paraphrastic or transformative-interpretive analysis, to separate it not only from the decompositional but the regressive conception as well, the latter characterizing the analysis-synthesis method of ancient Greek geometry. The present paper first locates logical analysis (as a philosophical method) in this picture, by proposing to define it as a definite kind of paraphrastic (or transformative/interpretive) analysis, the kind where the language in the analysans position is a logical language. The paper highlights the fact that the whole point of analyzing natural language forms by means of translating them into a logical language is essentially solving problems of inference – i.e. determining whether certain natural language sentence types follow from/contradict with/entail certain others, and shows that this particular conception of analysis is not as self-consistent a notion as it may first seem to be. The key to the argument is the undeniable connection between the meaning (at large) of a sentence form and the principal inferential relations that the sentence form is supposed (pre-analytically) to enter into with other related forms. The argument employs as a clear example the common modern logical analysis of the so-called problem of existential import of traditional categorical forms in order to problematize the viability of logical analysis, and concludes that one of the necessary conditions of the self-consistency (viability, possibility) of logical analysis is the acceptance of a certain negative thesis about meaning.

The Modal Verb ‘Shall’ in English Translations of Thai Legal Texts: The Plain Legal English Point of View

This study was designed to investigate the translation of laws from Thai into English with a focus on the use of modal ‘shall’ in the translations. As the trend of plain legal English continues to rise in the contemporary legal environment, some legal practitioners – lawyers, judges and legal drafters – have perceived that the use of the modal verb ‘shall’ (or the modal ‘shall’) may seem inappropriate and even to some extent, unclear in legal texts. Observing this phenomenon ultimately led to the formation of this study, and in following this line of argument, the research focuses on the extent to which the modal ‘shall’ is used by translators in the English translated versions of Thai laws, thus raising the research question: in which circumstances and to what extent do translators use the modal ‘shall’ in law translations from Thai into English? The study mainly adopted the concept of translation equivalence, focusing on the source-text and target-text clauses and sentences of the legal language, as an overall theoretical framework. The concept helps to indicate the extent to which the modal ‘shall’ is used in the English translations. As for the data, a total of 17 Thai laws (published by the Office of Council of the State of Thailand) and their corresponding translations were collected and used. The clauses where the modal ‘shall’ is used were then sorted out to create a parallel corpus for the analysis. The findings show that the modal ‘shall’ represents multifaceted denotative meanings of the verb in the Thai-source texts – referring to 1) obligation; 2) declaration of facts; and 3) futurity in Thai laws – which interestingly, deviate from the plain legal English movement. From the perspective of plain legal English, the modal ‘shall’ is only used to serve the language of obligation, often referring to a situation where ‘someone has a duty to do something’.

Explainable Meta-Path Based Recommender Systems

Meta-paths have been popularly used to provide explainability in recommendations. Although long/complicated meta-paths could represent complex user-item connectivity, they are not easy to interpret. This work tackles this problem by introducing a meta-path translation task. The objective is to translate a meta-path to its comparable explainable meta-paths that perform similarly in terms of recommendation but have higher explainability compared to the given one. We propose a definition of meta-path explainability to determine comparable explainable meta-paths and a meta-path grammar that allows comparable explainable meta-paths to be formed in a similar way as sentences in human languages. Based on this grammar, we propose a meta-path translation model, a sequence-to-sequence (Seq2Seq) model to translate a long and complicated meta-path to its comparable explainable meta-paths. Two novel datasets for meta-path translation were generated based on two real-world recommendation datasets. The experiments were conducted on these generated datasets. The results show that our model outperformed state-of-the-art Seq2Seq baselines regarding meta-path translation and maintained a better trade-off between accuracy and diversity/readability in predicting comparable explainable meta-paths. These results indicate that our model can effectively generate a group of explainable meta-paths as alternative explanations for those recommendations based on any given long/complicated meta-path.

The interaction of syntax, non-manuals, and prosodic cues as potential topic markers in Austrian Sign Language

Abstract Topic is often marked only by prosody across languages. In sign languages, prosody is expressed by features similar to those in speech: i.e., sign duration, velocity and amplitude of movement (cf. slope and range of pitch). Topicalized signs usually occur sentence-initially, are followed by a pause, and may show longer duration than nontopics. We used pausing and duration characteristics to resolve a puzzle concerning the status of OSV sentences in Austrian Sign Language (ÖGS): are they a nonmanually unmarked variant of non-manually marked topic sentences (O,SV), or are they a within-clause variant of the basic SOV order serving an as-yet-unknown discourse function? We investigated the temporal parameters of prosodic cues of 160 sentences produced by a fluent ÖGS signer: 40 in each of four conditions — SOV, OSV, S,OV, and O,SV. Overt topic marking effects on signing dynamics consisted of (1) lengthening of the sentence-initial topic phrases, causing a delay of the onset of the argument following the topic-marked item, and (2) a shortened duration of the noun phrase following the topic-marked item. Critically, the initial argument of OSV sentences did not show these prosodic cues, indicating that the initial argument O is not treated as being the same kind of topic as in the non-manually marked O,SV sentences. In accordance with the previous literature on pausing and lack thereof, we suggest that OSV might be akin to spoken German scrambling (within-clause movement), although the function performed remains to be investigated. The findings characterize parameters of temporal structure in the physical markers of topic in ÖGS and point to cross-linguistic variability in prosodic marking for topicalized structures.

Night Sleep, Parental Bedtime Practices and Language Development in Low-Risk Preterm and Full-Term Late Talkers: A Longitudinal Study in the Third Year of Life.

Background: Studies on night sleep and parental bedtime practices and their associations with language development in populations at risk of language delay and neonatal conditions, such as late talkers and preterm children, are scarce. Objectives: Our objective was to longitudinally examine the development of night sleep (total night sleep difficulties, settling, night waking, and co-sleeping), parental bedtime practices (total parental bedtime practices, active physical comforting, encouraging autonomy, and leaving to cry), and expressive language (word and sentence production), and their associations in low-risk preterm and full-term late talkers from 31 to 37 months of age. Methods: Parents of 38 late talkers, 19 low-risk preterm and 19 full-term children, completed the Italian versions of the Infant Sleep Questionnaire, the Parental Interactive Bedtime Behavior Scale, and the MacArthur-Bates Communicative Development Inventory Words and Sentences Long Form. Results: Late talkers' night sleep difficulties, such as settling to sleep and night waking, decreased over time, with low-risk preterm late talkers experiencing more night waking and co-sleeping than full-term peers. Parents reported that instances of active physical comforting and leaving to cry also decreased, with parents of low-risk preterm late talkers reporting higher active physical comforting scores than parents of full-term peers. Improvements in parental practices of encouraging autonomy were significantly associated with increased sentence production from 31 to 37 months. Conclusions: Findings highlight the importance of monitoring night sleep in preterm and full-term late talkers. They also suggest that populations vulnerable to sleep and language delays may particularly benefit from targeted interventions promoting autonomy in their bedtime routines, which, in turn, could support their language development trajectories.

Proficiency of Turing test on Nigerian Major Languages; Yoruba, Igbo and Hausa Using Google Translate

Google Translate performance evaluation in translating between English and three Nigerian languages: Yoruba, Igbo, and Hausa, highlighting its strengths and limitations using the Turing Test. The study assessed whether participants could distinguish Artificial Intelligence (AI) - generated translations from human translations which reveals that Google Translate handles basic communication in these languages but struggles with more complex linguistic features. The tool adequately translates straightforward sentences in Yoruba language, but falters with idiomatic expressions, proverbs, and tonal nuances, the same problem refects in Igbo language facing challenges with more complex sentences and dialectal variations, resulting in culturally irrelevant outputs. Hausa performed better due to the availability of a larger training dataset, delivering more accurate translations for common phrases, but still struggled with cultural and contextual nuances.The findings suggest that Google Translate's limitations prevent it from passing a Turing Test with native speakers of Yoruba, Igbo, and Hausa. While understandable translations are produced for basic sentences, native speakers can easily detect inconsistencies, awkward phrasing, and a lack of cultural nuance. These issues underscore the significant gap between AI translation and human-like proficiency for African languages. Keyword: Turing test, Nigerian Major Languages; Yoruba, Igbo, Hausa Using Google Translate Abiola, O.A., Adebisi R.O. & Akinola, S.O. (2024): Proficiency of Turing test on Nigerian Major Languages; Yoruba, Igbo and Hausa Using Google Translate. Journal of Advances in Mathematical & Computational Science. Vol. 12, No. 3. Pp 1-12 Available online at www.isteams.net/mathematics-computationaljournal. dx.doi.org/10.22624/AIMS/MATHS/V12N4P1

Evaluating What Others Say: The Effect of Accuracy Assessment in Shaping Mental Models of AI Systems

Forming accurate mental models that align with the actual behavior of an AI system is critical for successful user experience and interactions. One way to develop mental models is through information shared by other users. However, this social information can be inaccurate and there is a lack of research examining whether inaccurate social information influences the development of accurate mental models. To address this gap, our study investigates the impact of social information accuracy on mental models, as well as whether prompting users to validate the social information can mitigate the impact. We conducted a between-subject experiment with 39 crowdworkers where each participant interacted with our AI system that automates a workflow given a natural language sentence. We compared participants' mental models between those exposed to social information of how the AI system worked, both correct and incorrect, versus those who formed mental models through their own usage of the system. Specifically, we designed three experimental conditions: 1) validation condition that presented the social information followed by an opportunity to validate its accuracy through testing example utterances, 2) social information condition that presented the social information only, without the validation opportunity, and 3) control condition that allowed users to interact with the system without any social information. Our results revealed that the inclusion of the validation process had a positive impact on the development of accurate mental models, especially around the knowledge distribution aspect of mental models. Furthermore, participants were more willing to share comments with others when they had the chance to validate the social information. The impact of inaccurate social information on altering user mental models was found to be non-significant, while 69.23% of participants incorrectly judged the social information accuracy at least once. We discuss the implications of these findings for designing tools that support the validation of social information and thereby improve human-AI interactions.

Second Language Sentence Stress Assignment: Self‐ and Other‐Assessment

AbstractResearch on second language (L2) pronunciation self‐assessment reports a general misalignment between self‐ and other‐assessment. This has been attributed to the object of self‐assessment, the self‐assessment task, the measures to which self‐assessment is compared, and speakers’ characteristics. Here, we examined self‐assessment of a discrete phonological feature—sentence stress—by L2 English speakers as compared to the assessment of first language English listeners through a timed, forced‐choice judgment task with low‐pass filtered stimuli, which contained only suprasegmental cues. Additionally, we explored how individual differences among speakers predict self‐assessment. Speakers generally overestimated their accuracy in sentence stress assignment in a pattern resembling the Dunning‐Kruger effect despite the controlled nature of the task. Speakers with larger vocabulary size judged their sentence stress assignment as correct more often and showed greater overconfidence and miscalibration. Finally, the assessments of speakers with a background in applied linguistics and/or language teaching were more aligned with listeners’ assessments.

The Limola Language and Its Syntax: Unveiling the Functions, Structure, and Categories

Investigating the linguistic system of a regional language offers significant advantages from both the linguistic and cultural conservation perspectives. By examining a language's linguistic system, researchers can identify specific patterns employing subjects, verbs, objects, and other sentence elements while also documenting and preserving the language. Analyzing the syntax of the Limola language can provide new insights into general syntactic theory. The purpose of this study was to describe the syntactic functions, categories, and roles of Limola using a syntactic typology approach, clearly illustrating its syntactic functions, categories, and roles. The data for this study comprised phrases, clauses, and sentences in the Limola language, collected through instruments completed by sources and informants from two villages: Sassa and Makumpa. Data collection methods involved observation (survey), elicitation, and documentation. In drawing conclusions and discussions, triangulation presents the findings based on relevant theories. The results indicate that the syntactic categories in Limola are divided into five main categories: verbs, adjectives, adverbs, nouns, and numerals, as well as two categories of function words, namely prepositions/conjunctions and pronouns. Each category plays a vital role in shaping Limola’s meaning and sentence structure.

Fully exploring object relation interaction and hidden state attention for video captioning

Video Captioning (VC) is a challenging task of automatically generating natural language sentences for describing video contents. As a video often contains multiple objects, it is comprehensively crucial to identify multiple objects and model relationships between them. Previous models usually adopt Graph Convolutional Networks (GCN) to infer relational information via object nodes, but there exist uncertainty and over-smoothing issues of relational reasoning. To tackle these issues, we propose a Knowledge Graph based Video Captioning Network (KG-VCN) by fully exploring object relation interaction, hidden state and attention enhancement. In encoding stages, we present a Graph and Convolution Hybrid Encoder (GCHE), which uses an object detector to find visual objects with bounding boxes for Knowledge Graph (KG) and Convolutional Neural Network (CNN). To model intrinsic relations between detected objects, we propose a knowledge graph based Object Relation Graph Interaction (ORGI) module. In ORGI, we design triplets (head, relation, tail) to efficiently mine object relations, and create a global node to enable adequate information flow among all graph nodes for avoiding possibly missed relations. To produce accurate and rich captions, we propose a hidden State and Attention Enhanced Decoder (SAED) by integrating hidden states and dynamically updated attention features. Our SAED accepts both relational and visual features, adopts Long Short-Term Memory (LSTM) to produce hidden states, and dynamically update attention features. Unlike existing methods, we concatenate state and attention features to predict next word sequentially. To demonstrate the effectiveness of our model, we conduct experiments on three well-known datasets (MSVD, MSR-VTT, VaTeX), and our model achieves impressive results significantly outperforming existing state-of-the-art models.

Effects of Task Instructions on Predictive Eye Movements and Word Recognition During Second Language Sentence Comprehension

AbstractThis study tested whether encouraging prediction enhances prediction in second language (L2) speakers. L2 English speakers listened to English sentences like The woman … will read/buy one of the newspapers while viewing the target (a newspaper) and distractor objects (a rose, a bowl, and a mango) on a screen and clicked on the target as quickly as possible. The target was predictable (read) or unpredictable (buy) from the verb meaning. Participants looked at the target longer and were quicker to move the mouse to it when instructed to predict sentence continuation than when they were merely instructed to comprehend sentences. This result held true both when the target was predictable and unpredictable. Furthermore, only when instructed to predict did the participants make more clicking errors when the target was unpredictable than predictable, which suggested that encouraging prediction can interfere with word recognition accuracy in unpredictable contexts due to reduced cognitive resources or failed predictions.

Perceptual representations in L1 and L2 spatial and abstract language processing: applying an innovative sentence-diagram verification paradigm.

Perceptual representations in language comprehension were examined using sentence-picture verification tasks. However, concerns have been raised regarding the suitability of concrete pictures for representing abstract concepts compared to image-schematic diagrams. To assess the perceptual representations of spatial and abstract domains in both first language (L1) and second language (L2) processing, the study tests bilingual speakers' mental imagery on the basis of the simulation-based L1 comprehension model and proposes a simulation-based L2 comprehension model, supported by empirical evidence from an innovative sentence-diagram verification paradigm. 41 adult L1 Mandarin Chinese speakers participated in the study. 21 participants completed the Chinese sentence-diagram verification task (Experiment 1), while 20 participants completed the translation-equivalent version in L2 English (Experiment 2). Participants read a sentence [e.g., A diligent worker walked into the office (spatial sense); A strong team headed into the final (abstract sense)] at their self-paced speed, followed by a congruent (e.g., into diagram) or incongruent diagram (e.g., out-of diagram), and made binary judgments to verify spatial configurations between the sentence and diagram. Semantic rating tasks in both Chinese and English were also conducted to validate congruency between diagrams and sentences in both languages. Results from Experiment 1 indicate overall compatibility effects on L1 Chinese processing, unaffected by directional verbs or abstractness of sense. Results from Experiment 2 reveal interference effects on L2 English processing, with interference observed only after reading sentences encoding spatial senses, not abstract senses. Aligning with previous findings using sentence-picture verification tasks, the current findings confirm the weaker mental simulation effects in L2 processing compared to L1 processing. These findings extend the existing simulation-based L1 comprehension model, provide empirical support for the proposed simulation-based L2 comprehension model, and validate the innovative sentence-diagram verification paradigm for examining image-schematic representations in spatial and abstract language processing among Chinese-English bilinguals. The paradigm holds significant potential for research on perceptual representations in processing a broader range of grammatical and semantic properties during both online and offline L1 and L2 comprehension.

Al Jinās Dan Al Saja’ Fii Kitāb Washāya Al Abā Li Al Abnā

<p style="text-align: justify;"><span>The study of rhetoric is one of the sciences that studies how to process words or sentences in the Arabic language that are beautiful because they have clear meanings, and the style of the language used must be in accordance with the situation and conditions. There are three divisions in rhetoric: the science of meanings, the science of eloquence, and the science of ingenuity. In the science of ingenuity, the researcher chose alliteration and assonance. The aims of this study are: 1.Definition How many Jinas and Sajak in the book of commandments of fathers to children. 2.Definition of the types of Jinas and Sajak in the book Fathers' Commandments to Sons. The library study is based on reading books, magazines, or other research sources to collect data, and the researcher needs deep thinking to get a good research result, then she reads the books and continues to analyze the books in terms of rhetoric.</span></p>

Far transfer of retrieval-practice benefits: rule-based learning as the underlying mechanism.

Benefits of self-testing for learning have been consistently shown for simple materials such as word lists learned by rote memorization. Considerably less evidence for such benefits exists for complex, more educationally relevant materials and its application to new situations. The present study explores the mechanisms underlying this transfer. To this end, a typical retrieval-practice-effect paradigm was applied to foster the learning of an artificial language. Participants either repeatedly studied grammatically correct exemplar sentences of the artificial language or engaged in a cloze test as the interim testafter learning. To assess far transfer, participants in both groups of restudy and retrieval practice engaged in a grammaticality judgment test after a delay of 5min and 1week. In addition, participants in both groups completed a final memory test (i.e., a cloze test identical to the initial test) 1week after learning. In addition to a long-term memory benefit of retrieval practice,results revealed alsoa retrieval-practicebenefit in the far-transfer test after the 1-week delay. The findings further support the view that far transfer is supported by learning the underlying grammatical rules as opposed to memorizing the material. Thus, retrieval practice is also effective for fostering learning of complex materials and, even more importantly, for promoting transfer of learning-a crucial goal in modern educational practices.

ImageSchemaNet: A framester graph for embodied commonsense knowledge

Commonsense knowledge is a broad and challenging area of research which investigates our understanding of the world as well as human assumptions about reality. Deriving directly from the subjective perception of the external world, it is intrinsically intertwined with embodied cognition. Commonsense reasoning is linked to human sense-making, pattern recognition and knowledge framing abilities. This work presents a new resource that formalizes the cognitive theory of image schemas. Image schemas are dynamic conceptual building blocks originating from our sensorimotor interactions with the physical world, and enable our sense-making cognitive activity to assign coherence and structure to entities, events and situations we experience everyday. ImageSchemaNet is an ontology that aligns pre-existing resources, such as FrameNet, VerbNet, WordNet and MetaNet from the Framester hub, to image schema theory. This article describes an empirical application of ImageSchemaNet, combined with semantic parsers, on the task of annotating natural language sentences with image schemas.

ViSL model: The model automatically generates sentences of Vietnamese sign language

ViSL model: The model automatically generates sentences of Vietnamese sign language