Model Of Language Research Articles

Blame-Correct Support for Receiver Properties in Recursively-Structured Actor Contracts

Actor languages model concurrency as processes that communicate through asynchronous message sends.Unfortunately, as the complexity of these systems increases, it becomes more difficult to compose and integrate their components. This is because of assumptions made by components about their communication partners which may not be upheld when they remain implicit. In this paper, we bring design-by-contract programming to actor programs through a contract system that enables expressing constraints on receiver-related properties.Expressing properties about the expected receiver of a message, and about this receiver's communication behavior, requires two novel types of contracts.Through their recursive structure, these contracts can govern entire communication chains. We implement the contract system for an actor extension of Scheme, describe it formally, and show how to assign blame in case of a contract violation.Finally, we prove our contract system and its blame assignment correct by formulating and proving a blame correctness theorem.

Building a Speech Dataset and Recognition Model for the Minority Tu Language

Speech recognition technology has many applications in our daily life. However, for many low-resource languages without written forms, acquiring sufficient training data remains a significant challenge for building accurate ASR models. The Tu language, spoken by an ethnic minority group in Qinghai Province in China, is one such example. Due to the lack of written records and the great diversity in regional pronunciations, there has been little previous research on Tu-language speech recognition. This work seeks to address this research gap by creating the first speech dataset for the Tu language spoken in Huzhu County, Qinghai. We first formulated the relevant pronunciation rules for the Tu language based on linguistic analysis. Then, we constructed a new speech corpus, named HZ-TuDs, through targeted data collection and annotation. Based on the HZ-TuDs dataset, we designed several baseline sequence-to-sequence deep neural models for end-to-end Tu-language speech recognition. Additionally, we proposed a novel SA-conformer model, which combines convolutional and channel attention modules to better extract speech features. Experiments showed that our proposed SA-conformer model can significantly reduce the character error rate from 23% to 12%, effectively improving the accuracy of Tu language recognition compared to previous approaches. This demonstrates the effectiveness of our dataset construction and model design efforts in advancing speech recognition technology for this low-resource minority language.

Prior Context and Individual Alpha Frequency Influence Predictive Processing during Language Comprehension.

The extent to which the brain predicts upcoming information during language processing remains controversial. To shed light on this debate, the present study reanalyzed Nieuwland and colleagues' (2018) [Nieuwland, M. S., Politzer-Ahles, S., Heyselaar, E., Segaert, K., Darley, E., Kazanina, N., et al. Large-scale replication study reveals a limit on probabilistic prediction in language comprehension. eLife, 7, e33468, 2018] replication of DeLong and colleagues (2015) [DeLong, K. A., Urbach, T. P., & Kutas, M. Probabilistic word pre-activation during language comprehension inferred from electrical brain activity. Nature Neuroscience, 8, 1117-1121, 2005]. Participants (n = 356) viewed sentences containing articles and nouns of varying predictability, while their EEG was recorded. We measured ERPs preceding the critical words (namely, the semantic prediction potential), in conjunction with postword N400 patterns and individual neural metrics. ERP activity was compared with two measures of word predictability: cloze probability and lexical surprisal. In contrast to prior literature, semantic prediction potential amplitudes did not increase as cloze probability increased, suggesting that the component may not reflect prediction during natural language processing. Initial N400 results at the article provided evidence against phonological prediction in language, in line with Nieuwland and colleagues' findings. Strikingly, however, when the surprisal of the prior words in the sentence was included in the analysis, increases in article surprisal were associated with increased N400 amplitudes, consistent with prediction accounts. This relationship between surprisal and N400 amplitude was not observed when the surprisal of the two prior words was low, suggesting that expectation violations at the article may be overlooked under highly predictable conditions. Individual alpha frequency also modulated the relationship between article surprisal and the N400, emphasizing the importance of individual neural factors for prediction. The present study extends upon existing neurocognitive models of language and prediction more generally, by illuminating the flexible and subject-specific nature of predictive processing.

Addressing the Role of “Linguistic Knowledge” in Global Englishes Pedagogy

AbstractOne key tenet of Global Englishes for Language Teaching (GELT) is that the native English speaker should no longer serve as the role model for second language (L2) English users. Such a view does not discount that some degree of linguistic knowledge is necessary for successful global communication. However, GELT scholarship has remained relatively silent on the process and product of L2 acquisition. In this paper, we propose integrating L2 acquisition theory into GELT pedagogical practices without relying on theoretical perspectives steeped in some form of native speakerism. At the heart of our discussion is the concept of linguistic knowledge–implicit (unconscious) and explicit (conscious)–emphasizing the role of explicit learning processes in GELT classrooms rather than adhering solely to the idealistic goal of implicit competence. Because L2 learners need to reflect on linguistic knowledge (repertoire) and strategies for effective global communication, we propose that their learning should aim to develop skill sets of using explicit and implicit knowledge dynamically and efficiently in varied communicative contexts. We argue that skill acquisition theory offers a useful framework for GELT, as it can account for the development of automatized explicit knowledge alongside implicit knowledge, aligning with GELT objectives of fostering multicompetent English users. We illustrate how this perspective can inform classroom activities such as shadowing and task repetition. These activities not only promote deliberate, explicit learning processes but also integrate seamlessly with awareness–raising activities commonly employed in GELT, enhancing learners' communication strategies and capacity for effective English use in global contexts.

ArabBert-LSTM: improving Arabic sentiment analysis based on transformer model and Long Short-Term Memory.

Sentiment analysis also referred to as opinion mining, plays a significant role in automating the identification of negative, positive, or neutral sentiments expressed in textual data. The proliferation of social networks, review sites, and blogs has rendered these platforms valuable resources for mining opinions. Sentiment analysis finds applications in various domains and languages, including English and Arabic. However, Arabic presents unique challenges due to its complex morphology characterized by inflectional and derivation patterns. To effectively analyze sentiment in Arabic text, sentiment analysis techniques must account for this intricacy. This paper proposes a model designed using the transformer model and deep learning (DL) techniques. The word embedding is represented by Transformer-based Model for Arabic Language Understanding (ArabBert), and then passed to the AraBERT model. The output of AraBERT is subsequently fed into a Long Short-Term Memory (LSTM) model, followed by feedforward neural networks and an output layer. AraBERT is used to capture rich contextual information and LSTM to enhance sequence modeling and retain long-term dependencies within the text data. We compared the proposed model with machine learning (ML) algorithms and DL algorithms, as well as different vectorization techniques: term frequency-inverse document frequency (TF-IDF), ArabBert, Continuous Bag-of-Words (CBOW), and skipGrams using four Arabic benchmark datasets. Through extensive experimentation and evaluation of Arabic sentiment analysis datasets, we showcase the effectiveness of our approach. The results underscore significant improvements in sentiment analysis accuracy, highlighting the potential of leveraging transformer models for Arabic Sentiment Analysis. The outcomes of this research contribute to advancing Arabic sentiment analysis, enabling more accurate and reliable sentiment analysis in Arabic text. The findings reveal that the proposed framework exhibits exceptional performance in sentiment classification, achieving an impressive accuracy rate of over 97%.

El lenguaje como un proceso colaborativo: una replicación sobre el modelo colaborativo de Clark (Schober y Clark 1989)

The present study attempts to replicate two experimental designs conducted by Clark and his colleagues in 1989, testing the collaborative model of language. In this study, the Tangram task is replaced by a bomb defusal task using the video game Keep Talking and Nobody Explodes. A total of 20 pairs of participants were analyzed based on time taken, number of errors, number of speaking turns, and word count. Similar to the original 1987 study, in another phase of the experiment, a third person (an overhearer) performed the task by listening to a recording of another pair deactivatingbombs. The results of this study indicate a reduction in the number of interventions and words across the trials, as well as a decrease in the number of errors and time spent. No significant differences were found between the performance of pairs carrying out the task and that of non-communicating listeners. These data suggest that language does indeed tend to follow a collaborative model based on common references.

Dostoevsky in the Plain Language: The Problems of Modern Literary Translation

The article is devoted to the functional analysis of one of the modern adaptive models of the national literary language — the plain language. This concept is actively discussed and implemented in modern translation practice today, including in numerous literary translations of the works of Dostoevsky, the most widely read Russian writer in the world. From a historical and typological point of view, the new translation strategy correlates with the principles of the Russian translation of the first book from Dostoevsky’s reading circle — “One Hundred and Four Sacred Stories of the Old and New Testaments,” as well as the New Testament published in 1823, which includes the writer’s hard labor Gospel that became a key precedent text in his post-reformation work. The article analyzes the work performed in the early 2000s. Japanese scientist Ikuo Kameyama translated the novel “The Brothers Karamazov,” which was a representative experience of modern artistic interpretation and adaptation of Dostoevsky’s work. Thanks to the translator’s orientation to the plain language, the novel of the Russian writer found its foreignspeaking readers and became an understandable and familiar text for them.

BatFix: Repairing language model-based transpilation

To keep up with changes in requirements, frameworks, and coding practices, software organizations might need to migrate code from one language to another. Source-to-source migration, or transpilation, is often a complex, manual process. Transpilation requires expertise both in the source and target language, making it highly laborious and costly. Languages models for code generation and transpilation are becoming increasingly popular. However, despite capturing code-structure well, code generated by language models is often spurious and contains subtle problems. We propose BatFix , a novel approach that augments language models for transpilation by leveraging program repair and synthesis to fix the code generated by these models. BatFix takes as input both the original program, the target program generated by the machine translation model, and a set of test cases and outputs a repaired program that passes all test cases. Experimental results show that our approach is agnostic to language models and programming languages. BatFix can locate bugs spawning multiple lines and synthesize patches for syntax and semantic bugs for programs migrated from Java to C++ and Python to C++ from multiple language models, including, OpenAI’s Codex .

Multilingual end-to-end ASR for low-resource Turkic languages with common alphabets

To obtain a reliable and accurate automatic speech recognition (ASR) machine learning model, it is necessary to have sufficient audio data transcribed, for training. Many languages in the world, especially the agglutinative languages of the Turkic family, suffer from a lack of this type of data. Many studies have been conducted in order to obtain better models for low-resource languages, using different approaches. The most popular approaches include multilingual training and transfer learning. In this study, we combined five agglutinative languages from the Turkic family—Kazakh, Bashkir, Kyrgyz, Sakha, and Tatar,—in order to provide multilingual training using connectionist temporal classification and an attention mechanism including a language model, because these languages have cognate words, sentence formation rules, and alphabet (Cyrillic). Data from the open-source database Common voice was used for the study, to make the experiments reproducible. The results of the experiments showed that multilingual training could improve ASR performances for all languages included in the experiment, except Bashkir language. A dramatic result was achieved for the Kyrgyz language: word error rate decreased to nearly one-fifth and character error rate decreased to one-fourth, which proves that this approach can be helpful for critically low-resource languages.

Semantic agreement and the dual model of language

Abstract This paper will show that agreement cannot be considered a purely narrow-syntactic phenomenon, i.e., a phenomenon which cannot be given a functional-semantic explanation. It will show that person-number-gender (PNG-)affixes on verbs and adjectives are semantically interpretable even in languages such as English and Swedish, where they at first sight seem to be completely redundant. Several instances of so-called semantic agreement will reveal that these PNG-affixes convey important meanings not overtly marked by their co-referential DPs. In some cases, this even holds for the opposite case, i.e., that the PNG-affixes displaying grammatical agreement with their co-referential DPs also contribute with meanings not conveyed by the latter. Attempts to explain away semantic agreement by assuming there to be phonologically null determiners that give the DPs the same PNG-values as the agreement affixes will be refuted by data that show that the nouns often cannot be modified by such elements. Instead of being uninterpretable features that must be deleted before the derivation is sent to the semantic component, it will be shown that PNG-affixes on verbs and adjectives function like pronominal affixes, i.e., that they are arguments of the verbs and adjectives. They can either be anaphoric, in which case they can display semantic agreement, agreeing with the lexical semantics of the referent rather than with the PNG-values of the co-referential DP, or cataphoric, in which case they behave like formal-subject pronouns, signifying the PNG-value of the following logical DP subject.

Speaker-based language identification for Ethio-Semitic languages using CRNN and hybrid features

ABSTRACT Natural language is frequently employed for information exchange between humans and computers in modern digital environments. Natural Language Processing (NLP) is a basic requirement for technological advancement in the field of speech recognition. For additional NLP activities like speech-to-text translation, speech-to-speech translation, speaker recognition, and speech information retrieval, language identification (LID) is a prerequisite. In this paper, we developed a Language Identification (LID) model for Ethio-Semitic languages. We used a hybrid approach (a convolutional recurrent neural network (CRNN)), in addition to a mixed (Mel frequency cepstral coefficient (MFCC) and mel-spectrogram) approach, to build our LID model. The study focused on four Ethio-Semitic languages: Amharic, Ge’ez, Guragigna, and Tigrinya. By using data augmentation for the selected languages, we were able to expand our original dataset of 8 h of audio data to 24 h and 40 min. The proposed selected features, when evaluated, achieved an average performance accuracy of 98.1%, 98.6%, and 99.9% for testing, validation, and training, respectively. The results show that the CRNN model with (Mel-Spectrogram + MFCC) combination feature achieved the best results when compared to other existing models.

Does learning from language family help? A case study on a low-resource question-answering task

Abstract Multilingual pre-trained models make it possible to develop natural language processing (NLP) applications for low-resource languages (LRLs) using the model of resource-rich languages (RRLs). However, the structural characteristics of the target languages can impact task-specific learning. In this paper, we investigate the influence of structural diversities of languages on the system’s overall performance. Specifically, we propose a customized approach to leverage task-specific data of low-resource language families via transfer learning from RRL. Our findings are based on question-answering tasks using the XLM-R, mBERT, and IndicBERT transformer models and Indic languages (Hindi, Bengali, and Telugu). On the XQuAD-Hindi dataset, the few-shot learning using Bengali improves the benchmark mBERT (F1/EM) score by +(10.86/7.87) and XLM-R score by +(3.84/4.42). Few-shot learning using Telugu has also improved the mBERT score by +(10.42/7.36) and +(3.04/2.72) for XLM-R. In addition, our model has demonstrated benchmark-compatible performance in a zero-shot setup with single-epoch task learning. This approach can be adapted for other NLP tasks for LRLs.

A case study on decompounding in Indian language IR

Abstract Decompounding is an essential preprocessing step in text-processing tasks such as machine translation, speech recognition, and information retrieval (IR). Here, the IR issues are explored from five viewpoints. (A) Does word decompounding impact the Indian language IR? If yes, to what extent? (B) Can corpus-based decompounding models be used in the Indian language IR? If yes, how? (C) Can machine learning and deep learning-based decompounding models be applied in the Indian language IR? If yes, how? (D) Among the different decompounding models (corpus-based, hybrid machine learning-based, and deep learning-based), which provides the best effectiveness in the IR domain? (E) Among the different IR models, which provides the best effectiveness from the IR perspective? This study proposes different corpus-based, hybrid machine learning-based, and deep learning-based decompounding models in Indian languages (Marathi, Hindi, and Sanskrit). Moreover, we evaluate the effectiveness of each activity from an IR perspective only. It is observed that the different decompounding models improve IR effectiveness. The deep learning-based decompounding models outperform the corpus-based and hybrid machine learning-based models in Indian language IR. Among the different deep learning-based models, the Bi-LSTM-A model performs best and improves mean average precision (MAP) by 28.02% in Marathi. Similarly, the Bi-RNN-A model improves MAP by 18.18% and 6.1% in Hindi and Sanskrit, respectively. Among the retrieval models, the In_expC2 model outperforms others in Marathi and Hindi, and the BB2 model outperforms others in Sanskrit.

I2R: Intra and inter-modal representation learning for code search

Code search, which locates code snippets in large code repositories based on natural language queries entered by developers, has become increasingly popular in the software development process. It has the potential to improve the efficiency of software developers. Recent studies have demonstrated the effectiveness of using deep learning techniques to represent queries and codes accurately for code search. In specific, pre-trained models of programming languages have recently achieved significant progress in code searching. However, we argue that aligning programming and natural languages are crucial as there are two different modalities. Existing pre-train models based approaches for code search do not effectively consider implicit alignments of representations across modalities (inter-modal representation). Moreover, the existing methods do not take into account the consistency constraint of intra-modal representations, making the model ineffective. As a result, we propose a novel code search method that optimizes both intra-modal and inter-modal representation learning. The alignment of the representation between the two modalities is achieved by introducing contrastive learning. Furthermore, the consistency of intra-modal feature representation is constrained by KL-divergence. Our experimental results confirm the model’s effectiveness on seven different test datasets. This paper proposes a code search method that significantly improves existing methods. Our source code is publicly available on GitHub.1

Czech medical coding assistant based on transformer networks

The International Classification of Diseases (ICD) hierarchical taxonomy is used for so-called clinical coding of medical reports, typically presented in unstructured text. In the Czech Republic, it is currently carried out manually by a so-called clinical coder. However, due to the human factor, this process is error-prone and expensive. The coder needs to be properly trained and spends significant effort on each report, leading to occasional mistakes.The main goal of this paper is to propose and implement a system that serves as an assistant to the coder and automatically predicts diagnosis codes. These predictions are then presented to the coder for approval or correction, aiming to enhance efficiency and accuracy. We consider two classification tasks: main (principal) diagnosis; and all diagnoses. Crucial requirements for the implementation include minimal memory consumption, generality, ease of portability, and sustainability.The main contribution lies in the proposal and evaluation of ICD classification models for the Czech language with relatively few training parameters, allowing swift utilisation on the prevalent computer systems within Czech hospitals and enabling easy retraining or fine-tuning with newly available data. First, we introduce a small transformer-based model for each task followed by the design of a transformer-based “Four-headed” model incorporating four distinct classification heads.This model achieves comparable, sometimes even better results, against four individual models. Moreover this novel model significantly economises memory usage and learning time. We also show that our models achieve comparable results against state-of-the-art English models on the Mimic IV dataset even though our models are significantly smaller.

Model pedagogi genre untuk mengembangkan keterampilan membaca pemahaman bahasa indonesia di sekolah dasar

This study aims to describe of genre pedagogy learning model on Indonesian language comprehension in elementary school. This research uses literature study method with descriptive analysis technique. In this study, researchers sought data from the number or relevant books on genre pedagogy learning model and reading comprehension in Indonesian language in elementary school. Genre pedagogy is a text-based learning model based on a teaching and learning cycle where teachers provide guidance and interaction to learners before learners create texts independently. In practice, this genre pedagogy is implemented using four stages, namely the initial stage of Building Knowledge, the second stage is modelling, the third stage is joint construction and the last stage, namely independent construction. The application of the genre pedagogy model can indirectly motivate learners to utilize various learning resources around them in reading comprehension, especially when learners build their own understanding.

EHMMQA: English, Hindi, and Marathi multilingual question answering framework using deep learning

Abstract Multilingual question answering (MQA) is an effective access to multilingual data to provide accurate and precise answers, irrespective of language. Although a wide range of datasets is available for monolingual QA systems in natural language processing, benchmark datasets specifically designed for MQA are considerably limited. The absence of comprehensive and benchmark datasets hinders the development and evaluation of MQA systems. To overcome this issue, the proposed work attempts to develop the EHMQuAD dataset, an MQA dataset for low-resource languages such as Hindi and Marathi accompanying the English language. The EHMQuAD dataset is developed using a synthetic corpora generation approach, and an alignment is performed after translation to make the dataset more accurate. Further, the EHMMQA model is proposed to create an abstract framework that uses a deep neural network that accepts pairs of questions and context and returns an accurate answer based on those questions. The shared question and shared context representation have been designed separately to develop this system. The experiments of the proposed model are conducted on the MMQA, Translated SQuAD, XQuAD, MLQA, and EHMQuAD datasets, and EM and F1-score are used as performance measures. The proposed model (EHMMQA) is collated with state-of-the-art MQA baseline models for all possible monolingual and multilingual settings. The results signify that EHMMQA is a considerable step toward the MQA system utilizing Hindi and Marathi languages. Hence, it becomes a new state-of-the-art model for Hindi and Marathi languages.

Salvation, Enlightenment and Awakening in Shaping Followers' Perception and Understanding of Their Faith: A Linguistic Exploration

This paper examines how religious concepts of salvation, enlightenment, and awakening shape followers’ perception and understanding of their faith. Drawing on theories of language ideology (Woolard, 1998) and linguistic structuration (Giddens, 1979), it explores how linguistic and conceptual variations surrounding these key terms influence religious worldviews. A mixed methods approach integrates comparative-historical case studies, interviews, surveys, and textual analysis of three religious’ denominations. Findings reveal ideological divergences in the metaphorical framing of the key concepts, relating to differences in perceived resonances and definitions among followers. The study demonstrates that religious conceptual systems transmit between contexts via semiotic processes of indexicality and erasure, undergoing shifts in meaning. These ideological variations in turn contribute to divergent understandings of core principles across faith traditions. Ultimately, illuminating conceptual divergences and the symbolic processes by which they spread provides vital insight into religion’s cultural logic and diversity. The research contributes to theoretical models of religious language’s co-constitutive relationship with thought and experience. It offers an empirical approach employing cognitive linguistics and the sociology of religion to promote understanding across belief systems in an increasingly pluralistic world.

Preservation of sentiment in machine translation of low-resource languages: a case study on Slovak movie subtitles

AbstractThis research explores the effectiveness of machine translation from Slovak to English for sentiment analysis, specifically focusing on the translation of movie subtitles. The study employs a parallel corpus of segmented movie subtitles in both languages and utilizes IBM Watson™ Natural Language Understanding service and Google Translate. The research aims to assess the correlation between human-generated text and machine-translated text in sentiment analysis. A comparative analysis was also conducted using OpenAI to evaluate the sentiment of the Slovak text directly, without translation into English. The findings reveal a strong correlation between human text and machine translation, with a Pearson correlation coefficient of 0.86, and a correlation with OpenAI’s GPT model evaluation at 0.72. Despite the relatively high accuracy of the end-to-end solution using OpenAI, the methodology comprising machine translation followed by sentiment analysis in English was found to be significantly more precise. The research further investigates the challenges in translating specific language nuances, such as humor and vulgarism, and their impact on sentiment analysis. The study concludes that machine translation can be effectively used for sentiment analysis in Slovak, a flective language, and highlights the potential of advanced language models in low-resource languages. Future research directions include expanding the study to other text types and comparable languages beyond Slovak.

Semantically Enriched Cross-Lingual Sentence Embeddings for Crisis-related Social Media Texts

Tasks such as semantic search and clustering on crisis-related social media texts enhance our comprehension of crisis discourse, aiding decision-making and targeted interventions. Pre-trained language models have advanced performance in crisis informatics, but their contextual embeddings lack semantic meaningfulness. Although the CrisisTransformers family includes a sentence encoder to address the semanticity issue, it remains monolingual, processing only English texts. Furthermore, employing separate models for different languages leads to embeddings in distinct vector spaces, introducing challenges when comparing semantic similarities between multi-lingual texts. Therefore, we propose multi-lingual sentence encoders (CT-XLMR-SE and CT-mBERT-SE) that embed crisis-related social media texts for over 50 languages, such that texts with similar meanings are in close proximity within the same vector space, irrespective of language diversity. Results in sentence encoding and sentence matching tasks are promising, suggesting these models could serve as robust baselines when embedding multi-lingual crisis-related social media texts. The models are publicly available at: https://huggingface.co/crisistransformers.