Correct Sentences Research Articles

Deep Learning-Driven Sign Language Recognition: A Multimodal Approach for Gesture-to-Text Translation Using CNN-RNN Architectures

Sign language serves as a vital communication tool for individuals with hearing and speech impairments. However, a significant barrier exists when others do not possess a strong understanding of sign language often requiring interpreters to facilitate communication. To reduce the reliance on human interpreters, this research aims to develop an intelligent system capable of recognizing and translating sign language gestures into meaningful grammatically correct sentences. The proposed system processes both images and videos to interpret gestures, converting them into text that can be translated into any desired language. The system leverages state-of-the-art deep learning techniques, such as Convolutional Neural Networks (CNNs) for identifying key features in images and Recurrent Neural Networks (RNNs) for understanding temporal sequences in video data. By employing these advanced neural networks, the model is able to comprehend hand movements, facial expressions and other non-verbal cues to construct coherent sentences. Additionally, the system integrates natural language processing (NLP) to refine the output, ensuring the resulting sentences are grammatically correct. Our approach addresses common challenges such as differentiating between subtle hand gestures and reducing the impact of environmental noise in images. This solution holds the potential to significantly enhance communication for the hearing and speech impairedffering an efficient interpreter-free method of translating sign language into widely spoken languages

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.

Conflict Adaptation in Aphasia: Upregulating Cognitive Control for Improved Sentence Comprehension.

This study investigated conflict adaptation in aphasia, specifically whether upregulating cognitive control improves sentence comprehension. Four individuals with mild aphasia completed four eye tracking sessions with interleaved auditory Stroop and sentence-to-picture matching trials (critical and filler sentences). Auditory Stroop congruency (congruent/incongruent across a male/female voice saying "boy"/"girl") was crossed with sentence congruency (syntactically correct sentences that are semantically plausible/implausible), resulting in four experimental conditions (congruent auditory Stroop followed by incongruent sentence [CI], incongruent auditory Stroop followed by incongruent sentence [II], congruent auditory Stroop followed by congruent sentence [CC], and incongruent auditory Stroop followed by congruent sentence [IC]). Critical sentences were always preceded by auditory Stroop trials. At the end of each session, a five-item questionnaire was administered to assess overall well-being and fatigue. We conducted individual-level mixed-effects regressions on reaction times and growth curve analyses on the proportion of eye fixations to target pictures during incongruent sentences. One participant showed conflict adaptation indicated by faster reaction times on active sentences and more rapid growth in fixations to target pictures on passive sentences in the II condition compared to the CI condition. Incongruent auditory Stroop also modulated active-sentence processing in an additional participant, as indicated by eye movements. This is the first study to observe conflict adaptation in sentence comprehension in people with aphasia. The extent of adaptation varied across individuals. Eye tracking revealed subtler effects than overt behavioral measures. The results extend the study of conflict adaptation beyond neurotypical adults and suggest that upregulating cognitive control may be a potential treatment avenue for some individuals with aphasia. https://doi.org/10.23641/asha.27056149.

A hybrid approach for Bengali sentence validation

Bengali is the official language of Bangladesh and is widely used in Bangladesh and West Bengal in India. Due to the growing accessibility of the internet and smart devices, the use of digital text material and documents in Bengali is growing with time. An automated Bengali Sentence Validation System is proposed in this study to effectively determine the correctness of sentences in such extensively available Bengali content. As far as we know, no substantial work has been done in the field of Bengali Sentence Validation utilizing deep learning approaches. Due to the lack of linguistic resources, sophisticated Natural Language Processing tools, and benchmark datasets, developing an automated Sentence Validation System for a limited-resource language like Bengali is challenging. Additionally, Bengali Sentences come in two morphological varieties (Sadhu-bhasha and Cholito-bhasha), making the validation process more challenging. The proposed automated Bengali Sentence Validation system contains the CNN-BiLSTM hybrid classifier model. As of now, there is no standard dataset for Bengali sentence validation. Due to the lack of a standard dataset, we collected Bengali sentences from different sources in Bangladesh and developed a Bengali Sentence Validation (BSV) Dataset with around 5000 labelled sentences arranged into two categories such as correct and incorrect. Experimental results demonstrate that the proposed system outperformed other classifier models and existing approaches for Bengali Sentence Validation and is able to categorize a wide range of Bengali sentences based on their correctness. The system’s F1 score for the Bengali Sentence Validation is 98%.

NLPLego: Assembling Test Generation for Natural Language Processing Applications

With the development of Deep Learning, Natural Language Processing (NLP) applications have reached or even exceeded human-level capabilities in certain tasks. Although NLP applications have shown good performance, they can still have bugs like traditional software and even lead to serious consequences. Inspired by Lego blocks and syntax structure analysis, we propose an assembling test generation method for NLP applications or models and implement it in NLPLego . The key idea of NLPLego is to assemble the sentence skeleton and adjuncts in order by simulating the building of Lego blocks to generate multiple grammatically and semantically correct sentences based on one seed sentence. The sentences generated by NLPLego have derivation relations and different degrees of variation. These characteristics make it well-suited for integration with metamorphic testing theory, addressing the challenge of test oracle absence in NLP application testing. To validate NLPLego , we conduct experiments on three commonly used NLP tasks (i.e., machine reading comprehension, sentiment analysis, and semantic similarity measures), focusing on the efficiency of test generation and the quality and effectiveness of generated tests. We select five advanced NLP models and one popular industrial NLP software as the tested subjects. Given seed tests from SQuAD 2.0, SST, and QQP, NLPLego successfully detects 1,732, 3,140, and 261,879 incorrect behaviors with around 93.1% precision in three tasks, respectively. The experiment results show that NLPLego can efficiently generate high-quality tests for multiple NLP tasks to detect erroneous behaviors effectively. In the case study, we analyze the testing results provided by NLPLego to obtain intuitive representations of the different NLP capabilities of the tested subjects. The case study confirms that NLPLego can provide developers with clarity on the direction to improve NLP models or applications, laying the foundation for enhancing performance.

On relative word length and transposed-word effects.

It is harder to decide that a sequence of words is ungrammatical when the ungrammaticality is created by transposing two words in a correct sentence (e.g., he wants green these apples), and it is harder to judge that two ungrammatical word sequences are different when the difference is created by transposing two words (e.g., green want these he apples-green these want he apples). In two experiments, we manipulated the relative length of the transposed words such that these words were either the same length (e.g., then you see can it) or different lengths (e.g., then you create can it). The same-length and different-length conditions were matched for syntactic category and word frequency. In Experiment 1 (speeded grammatical decision) we found no evidence for a modulation of transposed-word effects as a function of the relative length of the transposed words. We surmised that this might be due to top-down constraints being the main driving force behind the effects found in the grammatical decision task. However, this was also the case in Experiment 2 (same-different matching with ungrammatical sequences of words) where syntactic constraints were minimized. Given that skilled readers can read sentences composed of words of the same length, our results confirm that word length information alone is not used to encode the order of words in a sequence of words, and especially concerning the order of adjacent words in foveal/parafoveal vision. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

PENDAMPINGAN BELAJAR SISWA SEKOLAH DASAR UNTUK MENINGKATKAN KEMAMPUAN CALISTUNG

This study focuses on the learning assistance at Rumah Baca Loloan aimed at improving the reading, writing, and arithmetic (calistung) skills of elementary school students. The background of this research is the concern over the low calistung abilities among elementary school students, which could potentially hinder their academic performance in the future. This assistance program is implemented in response to the urgent need to improve basic skills through a more personalized and supportive learning approach. The assistants, consisting of trained PGSD students, pay special attention to the students, ensuring they receive the necessary help to overcome learning difficulties. The results of the assistance show a significant improvement in the students' calistung abilities after participating in this program. Indicators of improvement include increased reading fluency and text comprehension, improved writing skills with correct spelling and sentence structure, as well as increased speed and accuracy in basic arithmetic. Additionally, there is an increase in students' motivation to learn and self-confidence. The significance of these findings is that learning assistance outside of formal school hours can play an important role in enhancing students' basic skills. Support from parents and the community also proves to be crucial in the success of this program. The importance of this service lies in the evidence that structured and sustained interventions can have a significant positive impact on students' academic development, highlighting the need to expand similar programs to reach more students in need.

Analisis Kesalahan Mahasiswa Dalam Menyelesaikan Soal Pembuktian Teorema Binomial Pada Sifat Simetrik

This research was conducted to observe students' error in working on problems proving the binomial theorem on the symmetric properties of binomial coefficients. This type of research is qualitative research. This research uses a test method, namely by working on questions followed by interviews. This research involved students from the Tadris Mathematics Study Program at Al Falah As Sunniyah Kencong Jember University in the second semester of the 2023/2024 academic year with a total of 17 students. The 17 students were grouped into 3 cognitive abilities, namely high, medium and low cognitive. The subjects of this research consisted of three students with each student representing each cognitive ability group. The results of the research show that the types of language interpretation errors are made by subjects with high and moderate cognitive abilities, namely not writing the correct sentences when drawing conclusions. Conceptual errors were made by subjects with medium and low cognitive abilities in the form of errors in the proof steps. Procedural errors were made by subjects with moderate and low cognitive abilities, namely not having carried out complete calculations when applying the theorem in numerical form. Technical errors were made by subjects with low cognitive abilities, namely writing the same steps repeatedly. Errors in drawing conclusions were made by subjects with high, medium and low cognitive abilities. The three subjects considered the verification process to be complete. All errors are caused by a lack of understanding of the concept, carelessness in the proof process and not being careful in reading the questions.

Flexible word position coding in reading: Roles for attention and memory.

Readers may fail to notice the error in 'Do love you me?': this is the recently established transposed-word (TW) phenomenon. Word position coding is a novel cognitive construct, and researchers are presently debating the underlying mechanisms. Here I investigated roles for attention and memory. Participants (N = 54) made grammaticality judgements to four-word sequences that formed correct sentences ('The man can run', 'The dog was here'), TW sentences ('The can man run', 'The was dog here'), or ungrammatical control sentences ('The man dog run', 'The was can here'). Sequences were replaced by post-masks after 200ms, and that post-mask was accompanied by a 50-ms retro-cue in the form of an 'X' presented at a critical location (where one could have locally inferred grammaticality; e.g., between the first and second word of 'The was dog here') or a non-critical location (e.g., between the third and fourth word of 'The was dog here'). TW sentences were harder to reject than control sentences - the classic TW effect - and crucially, this effect was modulated by cue validity, with valid cues attenuating TW effects compared to invalid cues. The present results suggest that focused attention aids the process of binding words to locations. Furthermore, as cues appeared after sentence offset, these results suggest that word position coding may take place in memory.

Using Response Prompting, Frames, and Technology to Teach Rural Students With Extensive Support Needs to Write About Stories

We used a single case concurrent multiple probe across participants design to evaluate the effects of a multi-component intervention package on the number of correct sentences used when writing about a story for three rural elementary students with intellectual or developmental disabilities and extensive support needs. The package was comprised of response prompting, sentence frames, and selection-based writing software and produced improved performance across all three participants. Overall, data indicate students improved their writing performance following the introduction of the package. We discuss our findings in relation to future research and implications for practitioners.

Generating Descriptive Text From Images - Image Caption Generator

In the modern era, image captioning has become one of the most widely required tools. Moreover, there are inbuilt applications that generate and provide a caption for a certain image, all these things are done with the help of deep neural network models. The process of generating a description of an image is called image captioning. It requires recognizing the important objects, their attributes, and the relationships among the objects in an image. It generates syntactically and semantically correct sentences. In this paper, we present a deep learning model to describe images and generate captions using computer vision and machine translation. This paper aims to detect different objects found in an image, recognize the relationships between those objects and generate captions. The dataset used is Flickr8k and the programming language used was Python3, and an ML technique called Transfer Learning will be implemented with the help of the caption model, to demonstrate the proposed experiment. This paper will also elaborate on the functions and structure of the various Neural networks involved. Generating image captions is an important aspect of Computer Vision and Natural language processing. Image caption generators can find applications in Image segmentation as used by Facebook and Google Photos, and even more so, its use can be extended to video frames. They will easily automate the job of a person who has to interpret images. Not to mention it has immense scope in helping visually impaired people

UNLOCKING SUCCESS: FOUR PHASE TECHNIQUES FOR PILING LARANG STUDENTS

Writing is a crucial life skill for success in classes, jobs, and community settings. Students write to learn in order to organize, investigate, and digest new information, as well as display their understanding (Klein et al., 2016). In general, the writing problems identified in this study through pre-test, interview, questionnaire, and observation were that students had difficulty producing a gramatically correct sentence, expressing their ideas logically, using appropriate words, producing cohesive and coherent text, and producing accurate spelling and punctuation. To solve those problems encountered by teachers in Binan City Senior High School, the researchers conducted a study on utilizing the four-phase technique to improve the writing skills of the students in Piling Larang Grade 12. This research employs a quasi-experimental research design utilizing the pretest and posttest to determine the effectiveness of the four-way phase technique to enhance the writing skills of the students in Grade 12 Piling Larang, and two sections were used for the comparison group and the experimental group with 30 students each. The noteworthy result of the inferential statistic of the research is that there is a significant difference between the posttest mean score of the experimental and comparison groups, which shows that there is a noteworthy increase in the mean score of the experimental group, proving that all theories in the literary reviews supported the result of this study. This research recommends that teachers may utilize the four-phase technique in improving their writing skills, and school administrators are encouraged to assist teachers in implementing the four-phase technique in writing. It encourages to hold seminars and workshops on this four-phase technique in writing, not only for senior high school teachers, and future researchers may identify and test other possible factors that can further define the effectiveness of the four-phase technique in improving the writing skills of students. KEYWORDS: Four phase technique, Writing Skills, Piling Larang, Quantitative Study

How L2 Learners Process Different Means of Time Encoding in a Tenseless Language: An ERP Study of Mandarin.

In the past, research on the cognitive neural mechanism of second language (L2) learners' processing time information has focused on Indo-European languages. It has also focused on the temporal category expressed by morphological changes. However, there has been a lack of research on L2 learners' various time coding means, especially for Mandarin, which lacks morphological changes. Using event-related potentials (ERPs), we investigated the cognitive neural mechanism of L2 learners with native Indonesian background in processing two time coding means (time adverbs and aspect markers) in Chinese. Indonesian has time adverb encoding time information similar to that of Chinese, but there are no aspect markers similar to Chinese in Indonesian. We measured ERPs time locked to the time adverb " (cengjing)" and the aspect marker "verb + (verb + guo)" in two different conditions, i.e., a control condition (the correct sentence) and a temporal information violation. The experimental results showed that the native speaker group induced the biphasic N400-P600 effect under the condition of time adverb violation, and induced P600 under the condition of the aspect marker "verb + (verb + guo)" violation. Indonesian L2 learners of Chinese only elicited P600 for the violation of time adverbs, and there was no statistically significant N400 similar to that of Chinese native speakers. In the case of aspect marker violation, we observed no significant ERPs component for the Indonesian L2 learners of Chinese. Both groups of subjects induced elicited a widely distributed and sustained negativity on the post-critical words after "verb + (verb + guo)" and "(cengjing)". This showed that the neural mechanism of Indonesian L2 learners of Chinese processing Chinese time coding differs from that of Chinese native speakers.

Mining core information by evaluating semantic importance for unpaired image captioning

Recently, exciting progress has been made in the research of supervised image captioning. However, manually annotated image-annotation pair data is difficult and expensive to obtain. Therefore, unpaired image captioning becomes an emerging challenge. This paper proposes a method called Mining Core Information by Evaluating Semantic Importance (MCIESI) for Unpaired Image Captioning, which is a method for image captioning using unpaired images and sentences. The main difference from the existing methods is that MCIESI focuses on mining the information that should be described in the image and embodies them in the generated natural language that conforms to human thinking. To achieve this goal, we use scene graphs to represent the semantics of images and evaluates the importance of objects and interaction relationships to mine core information in images, which are then encouraged to be embodied in generated sentences through semantic constraint. Combined with grammatical constraint using adversarial training with real sentence corpus and relative constraint using a triplet loss, the generator is trained to generate semantically plausible and grammatically correct sentences. Extensive experiments verify the effectiveness of MCIESI.

L’intuition dans l’apprentissage et l’emploi d’une langue étrangère : pourquoi la notion est-elle importante ?

The article discusses properties and the role of intuition in learning and using a foreign language. While intuition plays a crucial role in various cognitive processes, its specific implications in linguistics and foreign language pedagogy remain incompletely understood. The first part presents the main theses and research findings on intuition in linguistics (Chomsky, Guillaume, Benveniste, Langacker) and foreign language didactics (Krashen, Ellis, Oxford, DeKeyser, etc.). The second part outlines an empirical study of linguistic behaviours based on intuition. The study focuses on the evaluative function of intuition linked to the rapid judgment of the grammatical correctness of sentences and the compensation function, which involves the immediate inference of the meaning of unknown lexical units from the context. The study results, conducted with 102 students of French as a foreign language, show that at the level of intuitive behaviours concerning the morphological and syntactic subsystems, their linguistic reactions, fast and unconscious behaviours, are characterised by more significant correction than conscious behaviours. On the other hand, as far as the intuitive behaviour related to the lexical-semantic subsystem is concerned, better results are generated by longer reaction times. The study also demonstrated that intuition supports not only native users, but also foreign langue learners at intermediate level of proficiency.

DIFFICULTIES OF TRANSLATING POLYSEMOUS LEXICAL ITEMS AND THE STRATEGIES ADOPTED: A CASE STUDY OF EFL LEARNERS AT SABER FACULTY OF SCIENCE AND EDUCATION – DEPARTMENT OF ENGLISH - UNIVERSITY OF LAHIJ

This study aimed to investigate the knowledge of the undergraduate students in translating three Arabic polysemous lexical items into English in different contexts. The research aimed to assess the student's proficiency in giving the meaning of polysemous words and measure their ability to select appropriate equivalents as well as the strategies adopted. The study involved thirty students from the Department of English, Saber Faculty of Science and Education. The participants carried out a translation test that contextualized three Arabic lexical polysemous lexical items (ضرب - حصر- تخفيف) within fifteen sentences. Statistical analysis of the data revealed a lack of knowledge among undergraduate students as they faced difficulties in constructing proper sentence patterns and a lack of competence in finding the exact English equivalents, resulting in challenges in selecting words that formed coherent English sentences and violating the sentence structure. Consequently, their production of correct English sentences was compromised. Furthermore, the study identified that the students relied on various language strategies, including L1 transfer, synonymy, avoidance, and paraphrasing, to facilitate their translation process. However, these strategies led to unsatisfactory English translations.

A Systematic Survey of Automatic Image Description Generation Systems

Automatic image description generation is an active research area in Computer Vision and Natural Language Processing. Objects, their attributes, actions, and spatial relationships are identified in the image description generation system. Earlier, these systems used classical machine learning approaches. Later majority of these works follow deep learning strategies. The essential goal of these systems is to produce syntactically and semantically correct sentences. This review aims to synthesize the studies conducted from 2010 to 2023 to get a deeper view of various image description generation systems and their applications. The prominent contribution of this review is that it covers the different aspects of image captioning systems, such as the methods used, the various applied domains, evaluation measures, and the datasets used. A single synthesized study directs scholars regarding developing image captioning systems to date utilizing machine learning approaches. It also offers suggestions for researchers in this sector for the future. Image captioning is applied in many fields like natural images, medical images, remote sensing images, videos, etc. This review paper reviews the various taxonomies of image description generation systems. We also analyzed multiple methods used in the architecture of image captioning systems. The datasets and the evaluation metrics used in these systems are discussed. We studied the performance of the system under various circumstances.

Investigating the Grammatical and Lexical Coherence in Essay Writing by Iraqi EFL College Students

As the system of Arabic language is totally different from English, writing in English is considered a problematic issue for the Arab students including the Iraqi students as well. However, this problem can extend to university levels. Writing essays include not only writing correct sentences, but entail coherence in writing related to lexis, grammar and joining ideas in a coherent way. So, this article investigates the use of the grammar and lexical coherence in writing essays by Iraqi students at university level. This study is a primary data analysis of the responses obtained by two questionnaires set for the students and teachers. The study involves fifty students selected randomly from the third grade of Department of English at the College of Arts of University of Thi-Qar in Iraq. The students' answers and writing in essays are analyzed thoroughly to give the main findings. The analysis of the other questionnaire given to twenty teachers of the Departments of English of University of Thi-Qar comes in accordance with the results of the first questionnaire which set for the students and confirms the findings of the paper. The study reveals that Iraqi students have a good theoretical knowledge about types of essays and essay writings as well as conjunctions and coherence devices, yet they are weak in applying these devices in writing.

Transformer Models for Brazilian Portuguese Question Generation: An Experimental Study

Unlike tasks such as translation or summarization, generating meaningful questions necessitates a profound understanding of context, semantics, and syntax. This complexity arises from the need to not only comprehend the given text comprehensively but also infer information gaps, identify relevant entities, and construct syntactically and semantically correct interrogative sentences. We address this challenge by proposing an experimental fine-tuning approach for encoder-decoder models (T5, FLAN-T5, and BART-PT) tailored explicitly for Brazilian Portuguese question generation. Our study involves fine-tuning these models on the SQUAD-v1.1 dataset and subsequent evaluation, also on SQUAD-v1.1. Through our experimental endeavors, BART returned a higher result in all the ROUGE metrics, as ROUGE-1 0.46, ROUGE-2 0.24, and ROUGE-L 0.43, suggesting a higher lexical similarity in the questions generated, and it is comparable to the results of the question generation task for the English language. We explored how these advancements can significantly enhance the precision and quality of the question generation task in Brazilian Portuguese, bridging the gap between training data and the intricacies of interrogative sentence construction.

Explicitly Representing Syntax Improves Sentence-to-Layout Prediction of Unexpected Situations

Abstract Recognizing visual entities in a natural language sentence and arranging them in a 2D spatial layout require a compositional understanding of language and space. This task of layout prediction is valuable in text-to-image synthesis as it allows localized and controlled in-painting of the image. In this comparative study it is shown that we can predict layouts from language representations that implicitly or explicitly encode sentence syntax, if the sentences mention similar entity-relationships to the ones seen during training. To test compositional understanding, we collect a test set of grammatically correct sentences and layouts describing compositions of entities and relations that unlikely have been seen during training. Performance on this test set substantially drops, showing that current models rely on correlations in the training data and have difficulties in understanding the structure of the input sentences. We propose a novel structural loss function that better enforces the syntactic structure of the input sentence and show large performance gains in the task of 2D spatial layout prediction conditioned on text. The loss has the potential to be used in other generation tasks where a tree-like structure underlies the conditioning modality. Code, trained models, and the USCOCO evaluation set are available via Github.1