Language Sentences Research Articles

DG‐based SPO tuple recognition using self‐attention M‐Bi‐LSTM

This study proposes a dependency grammar-based self-attention multilayered bidirectional long short-term memory (DG-M-Bi-LSTM) model for subject–predicate–object (SPO) tuple recognition from natural language (NL) sentences. To add recent knowledge to the knowledge base autonomously, it is essential to extract knowledge from numerous NL data. Therefore, this study proposes a high-accuracy SPO tuple recognition model that requires a small amount of learning data to extract knowledge from NL sentences. The accuracy of SPO tuple recognition using DG-M-Bi-LSTM is compared with that using NL-based self-attention multilayered bidirectional LSTM, DG-based bidirectional encoder representations from transformers (BERT), and NL-based BERT to evaluate its effectiveness. The DG-M-Bi-LSTM model achieves the best results in terms of recognition accuracy for extracting SPO tuples from NL sentences even if it has fewer deep neural network (DNN) parameters than BERT. In particular, its accuracy is better than that of BERT when the learning data are limited. Additionally, its pretrained DNN parameters can be applied to other domains because it learns the structural relations in NL sentences.

Review Of Video Captioning Methods

Video captioning is the process of creating a natural language sentence that summarises the video's contents automatically. Modeling the video's effective temporal composition and effectively integrating that information into a plain language description are both required. It has a variety of applications, including assisting the visually impaired, video subtitling, and video surveillance, among others. Due to the advancement of deep learning in computer vision and natural language processing, there has been a surge in study in this area in recent years. Video captioning is the result of combining these two worlds of computer vision and natural language processing. In this study, we examine and analyse various strategies for addressing this issue, as well as benchmark datasets in terms of domains, repository size, and number of classes; and identify the benefits and drawbacks of various evaluation metrics such as BLEU, METEOR, CIDEr, SPICE, and ROUGE.

Attention-based quantum tomography

With rapid progress across platforms for quantum systems, the problem of many-body quantum state reconstruction for noisy quantum states becomes an important challenge. There has been a growing interest in approaching the problem of quantum state reconstruction using generative neural network models. Here we propose the ‘attention-based quantum tomography’ (AQT), a quantum state reconstruction using an attention mechanism-based generative network that learns the mixed state density matrix of a noisy quantum state. AQT is based on the model proposed in ‘Attention is all you need’ by Vaswani et al (2017 NIPS) that is designed to learn long-range correlations in natural language sentences and thereby outperform previous natural language processing (NLP) models. We demonstrate not only that AQT outperforms earlier neural-network-based quantum state reconstruction on identical tasks but that AQT can accurately reconstruct the density matrix associated with a noisy quantum state experimentally realized in an IBMQ quantum computer. We speculate the success of the AQT stems from its ability to model quantum entanglement across the entire quantum system much as the attention model for NLP captures the correlations among words in a sentence.

I3rab: A New Arabic Dependency Treebank Based on Arabic Grammatical Theory

Treebanks are valuable linguistic resources that include the syntactic structure of a language sentence in addition to part-of-speech tags and morphological features. They are mainly utilized in modeling statistical parsers. Although the statistical natural language parser has recently become more accurate for languages such as English, those for the Arabic language still have low accuracy. The purpose of this article is to construct a new Arabic dependency treebank based on the traditional Arabic grammatical theory and the characteristics of the Arabic language, to investigate their effects on the accuracy of statistical parsers. The proposed Arabic dependency treebank, called I3rab, contrasts with existing Arabic dependency treebanks in two main concepts. The first concept is the approach of determining the main word of the sentence, and the second concept is the representation of the joined and covert pronouns. To evaluate I3rab, we compared its performance against a subset of Prague Arabic Dependency Treebank that shares a comparable level of details. The conducted experiments show that the percentage improvement reached up to 10.24% in UAS and 18.42% in LAS.

Low Resource Neural Machine Translation: Assamese to/from Other Indo-Aryan (Indic) Languages

Machine translation (MT) systems have been built using numerous different techniques for bridging the language barriers. These techniques are broadly categorized into approaches like Statistical Machine Translation (SMT) and Neural Machine Translation (NMT). End-to-end NMT systems significantly outperform SMT in translation quality on many language pairs, especially those with the adequate parallel corpus. We report comparative experiments on baseline MT systems for Assamese to other Indo-Aryan languages (in both translation directions) using the traditional Phrase-Based SMT as well as some more successful NMT architectures, namely basic sequence-to-sequence model with attention, Transformer, and finetuned Transformer. The results are evaluated using the most prominent and popular standard automatic metric BLEU (BiLingual Evaluation Understudy), as well as other well-known metrics for exploring the performance of different baseline MT systems, since this is the first such work involving Assamese. The evaluation scores are compared for SMT and NMT models for the effectiveness of bi-directional language pairs involving Assamese and other Indo-Aryan languages (Bangla, Gujarati, Hindi, Marathi, Odia, Sinhalese, and Urdu). The highest BLEU scores obtained are for Assamese to Sinhalese for SMT (35.63) and the Assamese to Bangla for NMT systems (seq2seq is 50.92, Transformer is 50.01, and finetuned Transformer is 50.19). We also try to relate the results with the language characteristics, distances, family trees, domains, data sizes, and sentence lengths. We find that the effect of the domain is the most important factor affecting the results for the given data domains and sizes. We compare our results with the only existing MT system for Assamese (Bing Translator) and also with pairs involving Hindi.

Syntactic functioning of verbal phraseological units with atypical structure

The question concerning the syntax role of verbal phraseological units with an atypical structure is considered to be one of the complex issues of the syntax of a simple sentence (in their structure, such units do not have a verb component which may reflect such categories as type, tense, person, number). The aim of the research is to analyze the main patterns of the syntax functioning of verbal phraseological units with atypical structure in one- and two-parts sentences of the Belarusian language. The methodological base consists of the works of leading local and foreign linguists in phraseology and syntax. The material for the study is based on the factual material of the reputable phraseological dictionary of Mr. Lepeshev. The main methods are description, comparison, analytical research method, use of elements of quantitative calculations. The article considers the use issues of verbal phraseological units with an atypical structure in two-part sentences with a subject reflecting a person, a concrete or abstract subject. Mostly verb phraseological units are used in full sentences, but they can also be used in incomplete contextual ones. The author substantiates the use patterns of verbal phraseological units with an atypical structure which are used as a simple complexified verbal predicate in a two-parts sentence. The article also names and indicates factors influencing the functioning of verbal phraseological units in the role of linkers or copular parts. Being close in meaning to an infinitive, such units in combination with predicatives play the role of the main member of one-part impersonal sentences. The atypical functions of such units are the functions of definition, complement and adverbial of purpose. The article conclusions can be applied in lectures and practical classes in linguistics and cultural studies, as well as in the preparation of phraseological dictionaries.

Types of sentences in sign language

لەم توێژینەوەیەدا جۆرەکانی ڕستە لەزمانی ئاماژەدا خراونه‌تە ڕوو، لەڕێی شرۆڤەی وردی ڕستەکانەوە ڕووندەبێتەوە، کە هەمووجۆرەکانی ڕستە بەپێی ناوه‌ڕۆك و مەبەستەکان لەسیاقە جۆربەجۆرەکاندا خراونه‌ته‌ڕوو كه‌ نابیستان بۆ پەيوەندیکردن لەنێوان خۆیاندا و، پەیوەندیکردن بە دەوروبەرەوە بەکاریدەهێنن وپەیامەزمانییەجۆراوجۆرەکانی پێدەگوێزنەوە، ئەمەش ئەوەدەگەیەنێت کەزمانی ئاماژەی کوردی خاوەنی ڕێزمانێکی تایبەت بە خۆیەتی جیاوازە لەڕێزمانی زمانی گۆكراوی کوردی ،ئەمەش پێچەوانەی ئەو بۆچوونە هەڵەیەیە دەربارەی ڕێزمانی زمانی ئاماژە، کەدەگوترێت، ڕێزمانی زمانی ئاماژەی هەروڵاتێک لەسەرزمانە گۆكراوەکەی هەمان وڵات داده‌ڕێژرێتەوە، بێگومان ئەم بۆچوونەش بەبێ بوونی زانیاریی لەسەرزمانی ئاماژە سەری هەڵداوە. ئه‌م توێژینەوەيه‌ لەپێشەکی ودووبەش پێکهاتووە، بەشی یەکەمی توێژینەوەکە تەرخانکراوە بۆ چەند بابەتێک : پێناسەی چەند چەمکێک، ڕیزمان وئاستەکانی ڕێزمان و پێناسەى ڕستەو،خستنەڕووی بنەماسەرەکییەکانی ڕستە لەزمانی ئاماژەو دانانی یەکە ئاماژەییەکان لەزمانی ئاماژەدا. بەشی دووەمی توێژینەوەکە بۆخستنە ڕووی جۆرەکانی ڕستەیە لە ڕووی ناوەڕۆکەوە بەپشت بەستن بە نموونە لە زمانی گۆكراوو وەرگێڕانێتی بۆزمانی ئاماژە بەجێبەجێکردنی یاسای ڕێزمانی تایبەت بۆدروستکردنی جۆرەکانی ڕستە لەزمانی ئاماژەدا. لەپاش ئەنجام تێبينييه‌كان و سەرچاوەکان دێت

Interrelation of types of separate forms of logic and linguistic models and types of sentences of natural language

The article proposes an apparatus of logic and linguistic modeling for extracting knowledge from electronic text documents. Different types of forms of logic and linguistic models have been singled out for the formalized representation of complex sentences of natural language. The author formulates the rules and carries out the classification of separate forms of logic and linguistic models depending on syntactic constructions of sentences natural language. Such separate forms of logic and linguistic models are templates with certain logical operations, which are used depending on the punctuation marks and connecting words applied in sentences. The specific order of simple predicates depends on the logical connections between simple sentences in the structure of the complex one. This allows to develop an algorithm for further analysis of textual information and its recovery from formal models of knowledge representation. Figs.: 1. Refs.: 12 titles.

Translating Imperative Sentences from English into Indonesian: COVID-19 Outbreak Text

The imperative sentences in the source language are categorized as imperative instructions. World health organization instructs the general population not to over-take information when reading or searching for information about Corona Virus Disease 2019 because it affects our mentality. It also instructs to avoid using unhelpful coping strategies. The imperative structure rules for English and Indonesian are different. This paper aims to explore the translation techniques used in translating imperative sentences in the ‘Mental health and psychosocial considerations during the COVID-19 outbreak’ text into Indonesian. The authors use Molina and Albir’s (2002) translation techniques. Data collection is done by taking twenty-three the imperatives in the source language of thirty imperative sentences in the text. The imperative sentences in the SL which have infinitive verbs without to are followed by a noun, verb-ing, past participle, adjective, and second person reflexive pronoun. It is found the suffix -lah to emphasize the verb in the TL. Twenty-two data consist of positive imperative sentences and one negative imperative sentence. The results show that from the twenty-three data, the authors found that there were six translation techniques used by the authors as the translators, namely: compensation, establish equivalent, reduction, transposition, literal, and modulation. The authors used literal translation mostly, and it is done because the starting point of the imperative sentences are at the initial of the sentence and they are readable in the TL. The imperative is the starting of meaning for the source language and the target language. The readers are more acceptable to understand the context of the text. They are expected to implement the instructions in their daily lives.

ВОПРОС О СЛОЖНОПОДЧИНЕННОМ ПРЕДЛОЖЕНИИ С ПРИДАТОЧНОЙ ЧАСТЬЮ МЕСТА В РУССКОЙ СИНТАКСИЧЕСКОЙ НАУКЕ

The article is devoted to a comprehensive description of an issue related to the complex sentence with adverbial clause of place in Russian syntax. There isn’t unanimous opinion regarding the classification of complex sentences in modern Russian language. The term “subordinate clause” was used in the works of AH. Vostokov and N.I. Grech in the 19th century. Later, a classification, close to the present day, including additional (explanatory), attributive and adverbial clauses was proposed by I.I. Davidov. Owing to the works of F.I. Buslaev, the issue was developed further. Attempts to classify adverbial clauses belong to him. It is necessary to highlight A.M. Peshkovsky, V.A. Beloshapkov, V.V. Babaytsev and L.Yu. Maximov among the grammarians considering subordinate clauses. The structural-semantic principle in considering subordinate clauses was used by S.E. Kryuchkov and L.Yu. Maksimov. That principle (in contrast to the functional-semantic one) allows more comprehensive characterizing the sentence, since it considers the way the sentence is attached, while taking into account the semantic relations of the two parts of a complex sentence. The object of research is the analyzing of the functional and semantic types of complex sentences with the adverbial clause of place in modern Russian language on the material of Russian writers’ works.

Sign Language Interpreter using Deep Learning

Abstract: Sign Language is invaluable to hearing and speaking impaired people and is their only way of communicating among themselves. However, it has limitations with its reach as the rest of the people have no information regarding sign language interpretation. Sign language is communicated via hand gestures and visual modes and is therefore used by hearing and speaking impaired people to intercommunicate. These languages have alphabets and grammar of their own, which cannot be understood by people who have no knowledge about the specific symbols and rules. Thus, it has become essential for everyone to interpret, understand and communicate via sign language to overcome and alleviate the barriers of speech and communication. This can be tackled with the help of machine learning. This model is a Sign Language Interpreter that uses a dataset of images and interprets the sign language alphabets and sentences with 90.9% accuracy. For this paper, we have used an ASL (American Sign Language) Alphabet. We have used the CNN algorithm for this project. This paper ends with a summary of the model’s viability and its usefulness for interpretation of Sign Language. Keywords: Sign Language, Machine Learning, Interpretation model, Convoluted Neural Networks, American Sign Language

Description sentence between nominative and verbal and different grammarians in it

Arabic language is one of the richest languages in its grammatical rules. Linguistic research, in its broad sense, has no limit. The grammatical dispute is nothing but a kind of different views, and the task of the grammatical researcher when examining a sentence is to classify it and explain how it is constructed. The research aims to identify the reality of the adjectival sentence, and its position in the Arabic sentence. The research problem here is to identify the adjectival phrase either as nominative or verbal, or is it something else, as it has a double character represented in the difference in its pronunciation from its meaning. In pronunciation, it is a noun, but in the meaning it is a verb-like. The paper also examines the views of both the Basris and the Kufis in the issue, and their arguments. The paper provides what is permissible and what is not in the i’rab, the similarity between adjective and verb, and the difference of grammarians in the adjectival predicate. The paper executes the descriptive analytical method. The research findings are: 1- There are three types of sentences in Arabic language: the nominative sentence, verbal sentence, and the adjectival sentence. 2- The study manages to reconcile between the different views in the adjectival sentence in terms of both verbal and nominative sentences. 3- The study also justifies this division. 4- These findings help to make grammar easier for the learners to avoid the interpretation that weighed heavily on grammar.

COMPARATIVE-TYPOLOGICAL APPROACH TO THE STATUS OF SIMPLE AND COMPLEX SENTENCES

The article touches upon the topical problems of modern linguistics related to the question of the status of linguistic units in general, and of simple and complex sentences, in particular, as signs of a nominative and communicative nature; as well as upon their structural-semantic and functional-communicative semantic features and their constituent parts such as, for example, the subject, the predicate, the object, the adverbial modifier, the introductory part and the introductory component, which are peculiarly characterized, defined and classified by the author. Proceeding from an integral approach to linguistic phenomena in modern directions of linguistics, which make up its anthropocentric paradigm, the author tries to express his attitude to a number of controversial issues faced by specialists in the field of syntax. Approaching these issues from a comparative-typological point of view, the author argues that, like any significant unit of a language, a sentence and all its types and varieties from a constructive and communicative-pragmatic points of view, should be considered as integral monolithic signs like a word. Pushing off from this general linguistic regularity, the author approaches in a new way to such parts of the sentence, which perform the syntactic function of a part of a sentence and its extenders, establishes and reveals their substantial status within the framework of sentences of such modern languages as English, which represents a type of languages of a predominantly analytical system; Uzbek representing a type of languages of a predominantly agglutinative system, and Russian demonstrating a type of languages of a predominantly inflectional system. According to the author, there is a universal law, in line with which any significant linguistic unit used in a sentence, alone or in combination with another unit, can and should function as a specific syntactic part of it, therefore, within the framework of a sentence there is not and cannot be such a member that should not have a syntactic function in it. Based on this approach to the sentence and its members, the author characterizes and classifies the members of the sentence and their extenders from the linguo-cognitive and linguo-pragmatic points of view in a new way, in contrast to the traditional approach, according to which the extenders of the sentence are its parts, and the extenders of the parts cannot be members of the sentence. The article attempts also to clarify the question of the role and functions of the subject and predicate in the constructive structure of the sentence and their relationships with other parts of the sentence. As a result, a new taxonomy of a simple sentence, depending on the ability of the subject and predicate to create their “peaks” has been worked out. It is substantiated that the correct solution of the problems raised in the article contributes to the improvement of the state of learning and teaching languages and the process of translation from one language into another.

The sentence repetition task as a measure of sign language proficiency

AbstractSign language research is important for our understanding of languages in general and for the impact it has on policy and on the lives of deaf people. There is a need for a sign language proficiency measure, to use as a grouping or continuous variable, both in psycholinguistics and in other sign language research. This article describes the development of a Swedish Sign Language Sentence Repetition Test (STS-SRT) and the evidence that supports the validity of the test’s interpretation and use. The STS-SRT was administered to 44 deaf adults and children, and was shown to have excellent internal reliability (Cronbach’s alpha of 0.915) and inter-rater reliability (Intraclass Correlation Coefficient [ICC] = 0.900, p < .001). A linear mixed model analysis revealed that adults scored 20.2% higher than children, and delayed sign language acquisition were associated with lower scores. As the sign span of sentences increased, participants relied on their implicit linguistic knowledge to scaffold their sentence repetitions beyond rote memory. The results provide reliability and validity evidence to support the use of STS-SRT in research as a measure of STS proficiency.

Attention-based Sequence to Sequence Learning for Machine Translation of Low Resourced Indic Languages – A case of Sanskrit to Hindi

Deep Learning techniques are powerful in mimicking humans in a particular set of problems. They have achieved a remarkable performance in complex learning tasks. Deep learning inspired Neural Machine Translation (NMT) is a proficient technique that outperforms traditional machine translation. Performing machine-aided translation on Indic languages has always been a challenging task considering their rich and diverse grammar. The neural machine translation has shown quality results compared to the traditional machine translation approaches. The fully automatic machine translation becomes problematic when it comes to low-resourced languages, especially with Sanskrit. This paper presents attention mechanism based neural machine translation by selectively focusing on a particular part of language sentences during translation. The work shows the construction of Sanskrit to Hindi bilingual parallel corpus with nearly 10K samples and having 178,000 tokens. The neural translation model equipped with an attention mechanism has been trained on Sanskrit to Hindi parallel corpus. The approach has shown the significance of attention mechanisms to overcome long-term dependencies, primarily associated with low resources Indic languages. The paper shows the attention plots on testing data to demonstrate the alignment between source and translated words. For the evaluation of the translated sentences, manual score based human evaluation and automatic evaluation metric based techniques have been adopted. The attention mechanism based neural translation has achieved 88% accuracy in human evaluation and a BLEU score of 0.92 on Sanskrit to Hindi translation.

Think-Talk-Write Strategy to Improve the Ability to Compose Indonesian Sentence Structure in SLBN Cicendo Bandung, Indonesia

This study aims to see whether the Think-Talk-Write method improves deaf children's capacity to compose Indonesian language sentence structures. The research method used was an experimental method using the design of one group pretest-posttest. The data collection technique used was a writing test. This study was conducted on seven deaf children in class XI at special needs school in Cicendo, Bandung, Indonesia. The study's findings based on the comparison of pre-test and post-test scores showed an increase in post-test scores was higher in each aspect, namely in the elements of Think, Talk, and Writes. This evidence showed that the seven subjects being able to choose random words and then arrange the words into a sentence structure with the subject-verb-object sentence pattern. This research can be a recommendation for teachers regarding the use of learning strategies, especially the Think-Talk-Write strategy to help improve the ability to compose Indonesian sentence structures or other materials for deaf children.

Robots for Foreign Language Learning: Speaking Style Influences Student Performance.

Much previous research suggests that teachers’ individual characteristics may affect students’ performance; however, which factors are particularly helpful is as yet unclear and methodologically very difficult to assess. In this paper, we study the effects of robots’ speaking styles when instructing students on a task. 40 participants saw a brief video in which a robot presented its instructions either in a charismatic or a not so charismatic speaking style. Participants’ task was then to produce foreign language sentences on the basis of visualizations of the prosodic properties of these sentences. A subsequent analysis of participants’ productions shows that language learners’ performance was significantly better when the robot had delivered its instructions in a charismatic voice. The results suggest not only that a charismatic speaking style may be crucial for teachers in general and hence one of the factors causing the interpersonal variation between teachers, but also that students can benefit from instructions by robots delivered in a charismatic speaking style.

Formation of historical commenting competence in students – future teachers of the Russian language and literature

Introduction. The relevance of the research lies in the fact that historical commenting on linguistic phenomena is an important means of solving the problems of training language teachers, since it allows explaining modern linguistic facts, concepts, patterns by establishing their diachronic properties. The purpose of the study is to test a model of the formation of historical commenting competence in students – future teachers of the Russian language and literature. Materials and methods. The study was conducted from January to December 2020 at the Immanuel Kant Baltic Federal University. The sample included 90 people. Methods applied: comparative historical analysis of monuments of ancient Russian writing; expert assessments; methodology for determining the formation of linguistic competence of students (E.N. Piskunova); mathematical statistics (χ2-Pearson test). Research results. The program of the courses "Old Church Slavonic" and "History of the Russian literary language" included the study and comparative historical analysis of the monuments of ancient Russian writing. The results of testing the model showed statistically significant changes in the experimental group according to the following criteria: information-cognitive (χ2 = 20.3; p <0.01), activity-semantic (χ2 = 45; p <0.01), professional-communicative (χ2 = 8.5; p <0.05) and practice-oriented (χ2 = 21.1; p <0.01). Discussion and conclusion. A comparative analysis of the research results showed that teaching the historical syntax of the Russian language affects the formation of the competence of historical commenting in students – future language teachers and the level of their training. The developed methodological toolkit provides an analysis of the syntactic field of subject sentences of the Old Russian language and their historical commenting on the basis of the subject of the dictum and the subject of modus.

Analysis of rating scales: A pervasive problem in bilingualism research and a solution with Bayesian ordinal models

AbstractResearch in bilingualism often involves quantifying constructs of interest by the use of rating scales: for example, to measure language proficiency, dominance, or sentence acceptability. However, ratings are a type of ordinal data, which violates the assumptions of the statistical methods that are commonly used to analyse them. As a result, the validity of ratings is compromised and the ensuing statistical inferences can be seriously distorted. In this article, we describe the problem in detail and demonstrate its pervasiveness in bilingualism research. We then provide examples of how bilingualism researchers can employ an appropriate solution using Bayesian ordinal models. These models respect the inherent discreteness of ratings, easily accommodate non-normality, and allow modelling unequal psychological distances between response categories. As a result, they can provide more valid, accurate, and informative inferences about graded constructs such as language proficiency. Data and code are publicly available in an OSF repository at https://osf.io/grs8x.

Machine Translation System Based on Deep Learning

The translation fusion of multi-machine translation is a strategy that has been proposed very early to improve the quality of machine translation. It has been fully discussed under the framework of statistical machine translation, and further breakthroughs in performance have entered a bottleneck period. Since the fusion methods of existing methods are mostly limited to surface features and lack effective deep fusion strategies, this paper encodes source language sentences and multiple system translations separately to achieve fusion at the coding level. In the specific decoding, we limit the decoding space, so that the translation model can obtain a higher performance improvement on less data. When increasing the values of the two hyper-parameters α and β, the translation effect can be gradually improved. The performance of the model reaches the maximum when a and b are respectively set to 0.4, and the translation performance will decrease if it continues to increase.