Monolingual Settings Research Articles

From Endangerment to (Re)Emergence: Child Language, Acquisition, and Reclamation

What does it mean to acquire an endangered language as a child? How does acquisition manifest across different endangered language scenarios? We propose a view of language acquisition and transmission that encompasses a broad range of language endangerment contexts beyond monolingual settings and thus reframes language endangerment through insights from children's speech, across learning contexts, and in relation to community ideologies. We consider how children's language practices affect and are affected by two interrelated projects: (a) institutional definitions of language endangerment and (b) community processes of self-determination. We conclude that attention to child language teases apart the socially constructed dimensions of languages and linguistic boundaries and highlights the vitalities that emerge through children's participation in acts of language reclamation.

“I Should Only Use One Language”: A Case Study of Spanish-Speaking Emergent Bilingual Students’ Translanguaging Experiences in a Middle School Science Classroom

Despite the linguistic diversity in the United States, English-only norms persist in educational settings, placing culturally and linguistically diverse students at a disadvantage. Translanguaging, as a linguistic application of culturally relevant practice, utilizes students’ first and second languages to enhance understanding and communication. Guided by core questions and thorough qualitative analysis, this case study delves into middle school emergent bilingual students’ engagement and experiences with translanguaging in the science classroom within a monolingual school setting. Findings indicate that translanguaging positively impacts students’ comprehension and engagement in science learning, facilitating access to content and strengthening home–school connections. However, students’ attitudes toward translanguaging are influenced by the lack of institutional support and societal language ideologies, leading to mixed opinions about its effectiveness and preferences. The study emphasizes the importance of listening to students’ voices and advocating professional support to establish multilingual educational environments to maximize the advantages of translanguaging practices.

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.

Document language moderates the text-belief consistency effect

ABSTRACT When researching socioscientific topics, particularly on the Internet, readers face multiple texts that they must integrate into a coherent mental model. Previous research in monolingual settings has found that comprehension is biased toward readers’ prior beliefs (text-belief consistency effect). Considering that the Internet is multilingual, this experiment focused on the influence of document language on the text-belief consistency effect. Assuming differences in epistemic prestige among languages, we hypothesized that document language would act as a credibility cue and moderate the text-belief consistency effect. Seventy-four German university students read two conflicting texts either in their L2 English (higher epistemic prestige) or in their L1 German (lower epistemic prestige). Participants constructed stronger situation models for the belief-consistent text than for the belief-inconsistent text only when they read the texts in German. These results indicate that document language can act as a source characteristic that may reduce belief biases in comprehension.

Can cross-domain term extraction benefit from cross-lingual transfer and nested term labeling?

Automatic term extraction (ATE) is a natural language processing task that eases the effort of manually identifying terms from domain-specific corpora by providing a list of candidate terms. In this paper, we treat ATE as a sequence-labeling task and explore the efficacy of XLMR in evaluating cross-lingual and multilingual learning against monolingual learning in the cross-domain ATE context. Additionally, we introduce NOBI, a novel annotation mechanism enabling the labeling of single-word nested terms. Our experiments are conducted on the ACTER corpus, encompassing four domains and three languages (English, French, and Dutch), as well as the RSDO5 Slovenian corpus, encompassing four additional domains. Results indicate that cross-lingual and multilingual models outperform monolingual settings, showcasing improved F1-scores for all languages within the ACTER dataset. When incorporating an additional Slovenian corpus into the training set, the multilingual model exhibits superior performance compared to state-of-the-art approaches in specific scenarios. Moreover, the newly introduced NOBI labeling mechanism enhances the classifier’s capacity to extract short nested terms significantly, leading to substantial improvements in Recall for the ACTER dataset and consequentially boosting the overall F1-score performance.

Cross-lingual few-shot sign language recognition

There are over 150 sign languages worldwide, each with numerous local variants and thousands of signs. However, collecting annotated data for each sign language to train a model is a laborious and expert-dependent task. To address this issue, this paper introduces the problem of few-shot sign language recognition (FSSLR) in a cross-lingual setting. The central motivation is to be able to recognize a novel sign, even if it belongs to a sign language unseen during training, based on a small set of examples. To tackle this problem, we propose a novel embedding-based framework that first extracts a spatio-temporal visual representation based on video and hand features, as well as hand landmark estimates. To establish a comprehensive test bed, we propose three meta-learning FSSLR benchmarks that span multiple languages, and extensively evaluate the proposed framework. The experimental results demonstrate the effectiveness and superiority of the proposed approach for few-shot sign language recognition in both monolingual and cross-lingual settings.

Color-Based Metaphors in English and the Process of Metaphorization in Technical Texts: A Review Study

The study of the complicated processes taking place in technical texts could change not only the mechanism of a contemporary term formation, but also widen their meanings and extend spheres of their application in both monolingual and multilingual communicative settings. Accordingly, this review study aims at the elaboration of the use and classification of the color-based metaphorical terms in English as well as at the analysis of the tendency for metaphorization of technical texts in English. It further aims at explaining the tendencies in the development of technical texts that have caused changes in the development of technical vocabulary initiating the ongoing process of metaphorization and an extension of meaning of the existing and newly created terms. To this end, the complicated mechanisms underlying the emerging concepts of enduring metaphorization are explained and the processes of meaning formation, extension or compression are illustrated. In the end, as there are potential threats in the pattern of the color-based term creation in technical terms in English and their possible translation into other languages, some relevant caveats are provided.

Navigating Multilingualism and Blended Learning: Challenges and Strategies in Trilingual Child Development and Modern Educational Practices

This paper explores the challenges and strategies in trilingual child development and blended learning in modern education. It addresses the nurturing of trilingualism in children, especially in predominantly monolingual settings, and highlights the cognitive benefits of multilingualism, such as enhanced problem-solving and cognitive flexibility. The study also examines the effectiveness of blended learning, a combination of traditional classroom and online education, particularly relevant during the COVID-19 pandemic. It assesses this approach in enhancing engagement and performance in trilingual students, using the Community of Inquiry framework. The paper aims to contribute to the understanding of trilingual child development and inform educational practices tailored to multilingual students' needs.

Neural Representations of Non-native Speech Reflect Proficiency and Interference from Native Language Knowledge.

Learning to process speech in a foreign language involves learning new representations for mapping the auditory signal to linguistic structure. Behavioral experiments suggest that even listeners that are highly proficient in a non-native language experience interference from representations of their native language. However, much of the evidence for such interference comes from tasks that may inadvertently increase the salience of native language competitors. Here we tested for neural evidence of proficiency and native language interference in a naturalistic story listening task. We studied electroencephalography responses of 39 native speakers of Dutch (14 male) to an English short story, spoken by a native speaker of either American English or Dutch. We modeled brain responses with multivariate temporal response functions, using acoustic and language models. We found evidence for activation of Dutch language statistics when listening to English, but only when it was spoken with a Dutch accent. This suggests that a naturalistic, monolingual setting decreases the interference from native language representations, whereas an accent in the listener's own native language may increase native language interference, by increasing the salience of the native language and activating native language phonetic and lexical representations. Brain responses suggest that such interference stems from words from the native language competing with the foreign language in a single word recognition system, rather than being activated in a parallel lexicon. We further found that secondary acoustic representations of speech (after 200 ms latency) decreased with increasing proficiency. This may reflect improved acoustic-phonetic models in more proficient listeners.Significance Statement Behavioral experiments suggest that native language knowledge interferes with foreign language listening, but such effects may be sensitive to task manipulations, as tasks that increase metalinguistic awareness may also increase native language interference. This highlights the need for studying non-native speech processing using naturalistic tasks. We measured neural responses unobtrusively while participants listened for comprehension and characterized the influence of proficiency at multiple levels of representation. We found that salience of the native language, as manipulated through speaker accent, affected activation of native language representations: significant evidence for activation of native language (Dutch) categories was only obtained when the speaker had a Dutch accent, whereas no significant interference was found to a speaker with a native (American) accent.

What was the language of the judgment again?: Traces of bilingualism in monolingual judgments

Multilingualism is often perceived as a burden for judicial institutions that are used to holding proceedings in a specific language. However, by translating resources and evidence from a different language into the language of a proceeding, courts are enabled to write judgments based on a monolingual set of resources potentially with minimal or no reference to their source language. This article aims at uncovering this myth of judicial monolingualism, showing how multilingual courts act when indispensable resources are only available in a language different from that of the proceedings. By analysing monolingual criminal judgments delivered by Justices of the Peace in the bilingual (German/Italian) Province of South Tyrol, Italy, this article shows how, in citing case-law available only in Italian, traces of Italian are introduced in judgments written in German. Through a quantitative study, three approaches to deal with this issue have been analysed (i) collages between the two languages; (ii) direct quotation from Italian; and (iii) translation. The article critically analyses these three approaches and highlights an alternative approach, consisting in a combination between direct quotation from Italian and a translation into German, in order to guarantee both legal certainty and adequate understanding for all concerned parties.

Cross-lingual extreme summarization of scholarly documents

The number of scientific publications nowadays is rapidly increasing, causing information overload for researchers and making it hard for scholars to keep up to date with current trends and lines of work. Recent work has tried to address this problem by developing methods for automated summarization in the scholarly domain, but concentrated so far only on monolingual settings, primarily English. In this paper, we consequently explore how state-of-the-art neural abstract summarization models based on a multilingual encoder–decoder architecture can be used to enable cross-lingual extreme summaries of scholarly texts. To this end, we compile a new abstractive cross-lingual summarization dataset for the scholarly domain in four different languages, which enables us to train and evaluate models that process English papers and generate summaries in German, Italian, Chinese and Japanese. We present our new X-SCITLDR dataset for multilingual summarization and thoroughly benchmark different models based on a state-of-the-art multilingual pre-trained model, including a two-stage pipeline approach that independently summarizes and translates, as well as a direct cross-lingual model. We additionally explore the benefits of intermediate-stage training using English monolingual summarization and machine translation as intermediate tasks and analyze performance in zero- and few-shot scenarios. Finally, we investigate how to make our approach more efficient on the basis of knowledge distillation methods, which make it possible to shrink the size of our models, so as to reduce the computational complexity of the summarization inference.

MHaDiG: A Multilingual Humor-aided Multiparty Dialogue Generation in multimodal conversational setting

Building a multimodal humor-aided dialogue generation system in a multilingual setting is a very challenging task and it could even be more challenging in the Hindi language because of its morphological richness and free word order. Existing works only solved the problem of unimodal humorous dialogue generation, not humor-aided, in a monolingual setting. However, due to the tremendous growth in multimodal and multilingual content, there is a great demand to build multimodal humor-aided systems that support multilingual information access. To this end, in this paper, we propose a deep learning-based multimodal sentiment and emotion-aware humor-aided multiparty dialogue generation in multilingual (Hindi and English) setting. We evaluate our proposed approach (MHaDiG) on the recently released Sentiment, Humor, and Emotion-aware Multilingual Multimodal Multiparty Dataset (SHEMuD). Experimental results show the efficacy of MHaDiG and show the effect of multilingualism over monolingualism in the multimodal conversational setting.

Monolingual, multilingual and cross-lingual code comment classification

Code comments are one of the most useful forms of documentation and metadata for understanding software implementation. Previous research on code comment classification has focused only on comments in English, typically extracted from a few programming languages. This paper addresses the problem of code comment classification not only in the monolingual setting, but also in the multilingual and cross-lingual one, in order to examine whether they can outperform the traditional monolingual approach. To tackle this task, we introduce a novel, publicly available code comment dataset, consisting of over 10,000 code comments collected from software projects written in eight programming languages (C, C++, C#, Java, JavaScript/TypeScript, PHP, Python, and SQL). About half of them are written in Serbian while the other half are written in English. This dataset was manually annotated according to a newly proposed taxonomy of code comment categories. We fine-tune and evaluate multiple monolingual and multilingual pre-trained neural language models on the code comment classification task and compare their performances to several baselines. The best results for Serbian comments are obtained using the monolingual neural model BERTić, trained on Serbian and closely related languages. On the other hand, the optimal choice for English is the multilingual neural model multilingual BERT, which successfully extracts useful patterns from data in both languages. Although the cross-lingual setting shows some promise for simple binary classification, it has yet to reach sufficiently high performance levels for practical use. We also analyze model performance across different programming languages.

Emotion Detection in Code-Mixed Roman Urdu - English Text

Emotion detection is a widely studied topic in natural language processing due to its significance in a number of application areas. A plethora of studies have been conducted on emotion detection in European as well as Asian languages. However, a large majority of these studies have been conducted in monolingual settings, whereas little attention has been paid to emotion detection in code-mixed text. Specifically, merely one study has been conducted on emotion detection inRoman Urdu (RU)andEnglish (EN)code-mixed text despite the fact that such text is widely used in social media platforms. A careful examination of the existing study has revealed several issues which justify that this area requires attention of researchers. For instance, more than 37% of the messages in the contemporary corpus are monolingual sentences representing that a purely code-mixed emotion analysis corpus is non-existent. To that end, this study has scrapped 400,000 sentences from three social media platforms to identify 20,000 RU-EN code-mixed sentences. Subsequently, an iterative approach is employed to develop emotion detection guidelines. These guidelines have been used to develop a large RU-EN emotion detection (RU-EN-Emotion) corpus in which 20,000 sentences are annotated as Neutral or Emotion-sentence. The sentences having emotions are further annotated with the respective emotions. Subsequently, 102 experiments are performed to evaluate the effectiveness of six classical machine learning techniques and six deep learning techniques. The results show, (a) CNN is the most effective technique when used with GloVe embeddings, and (b) our developed RU-EN-Emotion corpus is more useful than the contemporary corpus, as it employs a two-level classification approach.

The effects of overhearing on vocabulary learning in ethnic majority and minority preschool children.

Research shows that infants and preschoolers can learn novel words equally well through addressed speech as through overhearing two adults. However, most of this research draws from samples of ethnic majority children. The current study compares word learning in preschoolers (M age = 5;6) with an ethnic minority and an ethnic majority background (N = 132). An experimenter of the majority group (representative for most teachers in Flemish education) told a story in three different interaction situations: Addressed Speech, Overhearing Classroom and Overhearing Two Adults. Results show that children of both ethnic groups learn novel words in Addressed Speech and in Overhearing Classroom equally well. However, minority children learned significantly fewer words in Overhearing Two Adults. This study suggests important differences in how ethnic majority and minority children learn through indirect speech in educational (monolingual) settings. In addition, the study scrutinizes the potential role of social identification in overhearing mechanisms.

Deep learning based sentiment analysis and offensive language identification on multilingual code-mixed data

Sentiment analysis is a process in Natural Language Processing that involves detecting and classifying emotions in texts. The emotion is focused on a specific thing, an object, an incident, or an individual. Although some tasks are concerned with detecting the existence of emotion in text, others are concerned with finding the polarities of the text, which is classified as positive, negative, or neutral. The task of determining whether a comment contains inappropriate text that affects either individual or group is called offensive language identification. The existing research has concentrated more on sentiment analysis and offensive language identification in a monolingual data set than code-mixed data. Code-mixed data is framed by combining words and phrases from two or more distinct languages in a single text. It is quite challenging to identify emotion or offensive terms in the comments since noise exists in code-mixed data. The majority of advancements in hostile language detection and sentiment analysis are made on monolingual data for languages with high resource requirements. The proposed system attempts to perform both sentiment analysis and offensive language identification for low resource code-mixed data in Tamil and English using machine learning, deep learning and pre-trained models like BERT, RoBERTa and adapter-BERT. The dataset utilized for this research work is taken from a shared task on Multi task learning DravidianLangTech@ACL2022. Another challenge addressed by this work is the extraction of semantically meaningful information from code-mixed data using word embedding. The result represents an adapter-BERT model gives a better accuracy of 65% for sentiment analysis and 79% for offensive language identification when compared with other trained models.

Teaching foreign languages at the pre-primary level in a monolingual setting: the case of Poland


 In Poland, teaching a foreign language to very young children, i.e. aged 6 and below, has had a relatively long tradition in both educational services and research, which grew out of strong parental aspirations for their children’s educational, and consequently vocational, achievements in the globalized job market.
 The goals of the paper are twofold: firstly, it aims to outline the evolution of research directions in language teaching methodology to very young learners (i.e. below 6) over the last three decades. In this respect three major strands are distinguished, i.e. the language acquisition approach, the pedagogical approach and the ecological approach. Secondly, Polish research in pre-primary education is going to be outlined against the backdrop of these major trends in applied linguistics. It is believed that some of the findings may bear relevance to other contexts in which very early teaching of foreign languages is being implemented.

Translanguaging as a social justice strategy: the case of teaching Chinese to ethnic minority students in Hong Kong

For the past two decades, a significant number of ethnic minority students from diverse racial, cultural, linguistic, and religious backgrounds have entered Chinese language classrooms in Hong Kong for the first time. Simultaneously, Chinese language teachers have come under criticism for their lack of understanding of diversity and their failure to integrate ethnic minority students academically and socially. However, there is little research on how these teachers can transform their educational beliefs, teaching techniques, and attitudes toward diversity and inclusion to respond effectively to the drastic changes taking place in their professional work. This study examines how a group of Chinese language teachers employed translanguaging as a social justice strategy to address the challenges of teaching minority students in a monolingual and assimilative educational setting in Hong Kong. Classroom observations show that teachers enacted a translanguaging stance, using students’ familiar semiotic resources to make their teaching more inclusive and equitable for ethnic minority students from low socioeconomic and religious backgrounds. Teachers reported becoming more aware of diversity in the classroom as well as of the social inequalities and racial discrimination outside of school. The study shows that criticism has been unfairly levied on Chinese language teachers in Hong Kong, who should not be held responsible for the social problems hindering ethnic minorities’ social mobility. Research should include a decolonial perspective to legitimize translanguaging as a social justice strategy for more transformative praxis in the education sector in postcolonial Hong Kong.

Shifting and Expanding Clause Combining Strategies in Heritage Turkish Varieties

Turkish is a language described as relying predominantly on non-finite subordination in the domain of clause combining. However, there are also strategies of finite subordination, as well as means of syndetic and asyndetic paratactic clause combining, especially in the informal settings. Clause combining is and has been one of the focal points of research on heritage Turkish (h-Turkish). One point is particularly clear: In comparison with the monolingual setting, finite means of clause combining are more frequent in h-Turkish in Germany, the U.S., and the Netherlands, while non-finite means of clause combining are less frequent. Overall, our results confirm the findings of earlier studies: heritage speakers in Germany and the U.S. prefer paratactic means of clause combining using connectors, as opposed to monolingual speakers. Our results also reveal that age (adolescents vs. adults) and register (informal vs. formal) significantly modulate the use of connectors. Moreover, we find that the shift in preferences in means of clause combining triggers an expansion in the system of connectors and leads to the development of new narrative connectors, such as o zaman and derken. The system of syndetic paratactic clause combining is expanding in heritage Turkish. This expansion calls for multifaceted modeling of change in heritage languages, which integrates language-internal factors (register), dynamics of convergence with the contact languages, and extra-linguistic factors (age and language use).

Performing the good (im)migrant: inclusion and expectations of linguistic assimilation

ABSTRACT This paper analyzes how language is framed as a route to full inclusion, particularly for unaccompanied asylum-seeking students labelled as disabled. It is based on a qualitative study carried out in the Italian city of Rome, which, although cosmopolitan, is often characterised by nationalistic political landscapes. The manuscript reveals how institutional biases (re: race, ability, and migration) about unaccompanied forced migrant youth, often manifested in their construction as disabled and foreign by local professionals, hinder the likelihood that they successfully participate in the life of the host society. The paper also shows how the inclusion of migrant and refugee students living in foster care homes is conceptualised as a violent integration into monolingual and monocultural education settings. Drawing on Disability Critical Race Studies and Raciolinguistics, this contribution analyzes how unaccompanied forced migrant students respond to their linguistic urgency, learn power majority languages, and simultaneously devalue their home language to perform the ‘good (im)migrant.’ Lastly, the contribution shows how processes of racialization of disabled unaccompanied asylum-seeking and refugee students influence how western communities perceive their linguistic capacity and effort.