English German Research Articles

Cool, Correct or Cordial?: Swiss Attitudes towards American, British and Swiss German English

Cool, Correct or Cordial?: Swiss Attitudes towards American, British and Swiss German English

Quantum mechanics: A historical-linguistic perspective

Quantum mechanics would not be quantum mechanics without the many technical terms which emerged a century ago but are still found in modern textbooks. The theory was mainly created by German-speaking physicists, which is reflected in German–English hybrid words such as “eigenvalue” and “eigenstate.” While “eigenvalue” was a neologism—such as was “quantum mechanics” itself—other terms in the quantum lexicon were metaphors adopted from everyday language, e.g., “spin” and “tunneling.” Conversely, a few of the specialized quantum terms were later reused in common language with new meanings. In today's parlance, to make a “quantum jump” has a meaning entirely different from the one in which the expression originated. This paper examines various linguistic aspects associated with the early development of quantum mechanics and its predecessor, the old quantum theory. For example, all physicists are familiar with the quantum concept of degeneracy. But the same term has also been employed to denote certain groups of people as “degenerate.” Is there any historical connection between the two widely different connotations?

Neighbourhood Density in Spoken Word Recognition: An Eye-Tracking Study

Bilingual acquisition research has so far focused on comparisons with monolingual children in terms of linguistic features, performance on standardised tests, input, etc. In contrast, new methodologies, such as eye-tracking, can offer a more detailed understanding of the way bilinguals use both language systems. Eye fixations provide evidence of online processing. This study investigates spoken word recognition in children and adults. Word retrieval is affected by the number of words that minimally differ from the target (neighbourhood). Previous research found that only bilingual adults activated a similar-sounding competitor from the other language. As children have been found to be sensitive to neighbourhood density from quite early in previous research, similar results might be expected. This study includes 56 subjects (11 German–English bilingual and 12 English monolingual children, aged 8; 21 English monolingual and 12 German–English bilingual adults). The subjects’ online processing of competing items for high- and low-density targets was compared. The results confirm that neighbourhood density affects word recognition in adults and children. The bilingual children activated same-language competitors as well as similar-sounding competitors from the other language. Adults and children differed in terms of latencies, and the monolinguals were more accurate than the bilingual subjects.

Oscillatory features of German–English sentence processing: Evidence from an EEG study

Aims and objectives: In this study, we investigated the neural oscillatory patterns occurring during sentence-level overt bilingual processing tasks on 46 German–English bilinguals with the intention of finding out if there were task-specific oscillations for this language pair. Methodology: Four tasks, including Language 2 (L2) listening, Language 1 (L1) speaking, L2 shadowing (i.e., repeating a segment of an utterance), and backward interpreting (i.e., orally translating L2 sentences to L1), were implemented with the technique of electroencephalography (EEG). Data and analysis: Data included demographic indices, neural oscillation metrics, and oral production scores from participants. Static spectral analysis, time–frequency analysis, principal components analysis, cross-frequency coupling, and inferential statistics were applied to the data. Findings: The sensory task and articulatory tasks differed greatly in the delta–theta band and the beta–gamma band in terms of time–frequency dynamics. Analyses also revealed variations in a frontal/prefrontal theta component and a distinct gamma component across the four tasks. Moreover, we found a right fronto-temporal theta–gamma coupling unique to backward interpreting. Originality: The task-wise comparison experiment paradigm has rarely been implemented in neuroimaging research on language, especially so in EEG research on bilingualism. Significance: The findings provide novel insights for the understanding of language conversion mechanism and may benefit the training of professional interpreters.

Christian Ludwig (1660–1728) and the beginnings of German/English lexicography

Christian Ludwig’s 1706 A dictionary English, German, and French and his 1716 Teutsch–Englisches Lexicon were the first bilingual dictionaries published in response to an emerging German interest in learning English. Setting the dictionaries in the wider context of European bilingual lexicography, I argue that such dictionaries are valuable sources for linguistic and cultural history, in parallel to the more widely studied monolingual dictionaries. Taking advantage of the digitisation of Ludwig’s dictionaries, I show that in his 1706 English–German dictionary, adapted from the English/French dictionaries of Abel Boyer to fill a gap he identified as a practising teacher, Ludwig did not merely translate, but made systematic changes. In his larger German–English dictionary of 1716, Ludwig took his word-list largely from Kramer’s German–Italian dictionary (1700–1702), but used his own judgement in structuring entries and choosing — as well as expanding the range of — examples, reflecting his experience as a teacher and translator. I suggest that the different approach taken in dictionaries by teachers of non-native learners to explaining word meaning, pronunciation, and usage has yielded sources which — newly accessible thanks to digitisation - will enrich our understanding of language change and language standardisation, and of cultural change and exchange, where I argue for the value of taking a cross-linguistic, comparative perspective.

Multimodal Machine Translation

In recent years, neural network machine translation, especially in the field of multimodality, has developed rapidly. It has been widely used in natural languages processing tasks such as event detection and sentiment classification. The existing multimodal neural network machine translation is mostly based on the autoencoder framework of the attention mechanism, which further integrates spatial-visual features. However, due to the ubiquitous lack of corpus and the semantic interaction between multimodalities, the quality of machine translation is difficult to guarantee. Therefore, this paper proposes a multi-modal machine translation model that integrates external linguistic knowledge. Specifically, on the encoder side, we adopt the pre-trained Bert model to be used as an additional encoder to integrate with the original text encoder and picture encoder. Under the cooperation of the three encoders, a better text representation and picture representation at the source end is generated. Besides, the decoder decodes and generates a translation based on the image and text representation of the source. To sum up, this paper studies the visual-text semantic interaction on the encoder side and the visual-text semantic interaction on the decoder side, and further improves the quality of translation by introducing external linguistic knowledge. We compared the performance of the multimodal neural network machine translation model with pre-trained Bert and other baseline models in English German translation tasks on the multi30k data sets. The results show that the model can significantly improve the quality of multimodal neural network machine translation, which also verifies the importance of integrating external knowledge and visual text semantic interaction.

Adelung’s English-German dictionary (1783, 1796)

Summary This article examines Johann Christoph Adelung’s English–German dictionary (1783, 1796). The dictionary deserves our attention because it was undertaken by the author of a ground–breaking German dictionary (Adelung 1774–86, 2 1793–1801), working from Samuel Johnson’s equally epoch–making English dictionary (1755, 41773). Yet the work has thus far been almost entirely overlooked. This article seeks to address that research gap. It reveals the very significant differences between the first dictionary volume (1783) and the second (1796), and argues that the second volume – for which a dictionary by Johannes Ebers is a major source (Ebers 1793–1794) – is almost certainly not the work of Adelung at all. Close analysis of the first volume nevertheless reveals the care that Adelung took with it, drawing not just on Johnson, but also on his own German dictionary and other sources, resulting in a dictionary that was innovative in English–German lexicography. I also show that Adelung’s experience of preparing the English–German dictionary informed his revision of his better–known German dictionary (1793–1801) in at least two concrete ways: his treatment of pronunciation and his use of metalinguistic labelling.

Ungainly lexical borrowing? The case of the Anglicism der Shitstorm in German

Abstract This paper addresses the origin, evolution, and explosive profusion of the German Anglicism der Shitstorm referring to social media outrage across all registers in German. The word Shitstorm was declared Anglicism of the Year 2011 in the context of a project initiated by a group of German linguists that was designed to characterize Anglicisms as a positive contribution to the German lexicon. Using detailed corpus analysis, this paper explains the origin of the English etymon, its evolution and resemanticization over time. By comparing data from news media corpora in English and German, this study explains why the Anglicism Shitstorm is not perceived as vulgar in German. While German linguists behind the Anglicism of the Year intended to change the negative perception of the increasing influence of English in contemporary German, this analysis explains why Anglicisms like Shitstorm in German can be problematic in international contexts. From a wider perspective on the semiotics of a general German Anglophilia, the profusion of the Anglicism Shitstorm in German is representative of precisely the social media practices the word is used to describe. The case of the Anglicism der Shitstorm in German is therefore symptomatic of the influence social media have on all media.

China English or Chinese English?

The distinction between ‘Singapore English’ and ‘Singaporean English’ is interesting. It appears that small places tend to use the bare name of the country, while larger countries use the derived adjective. So we find: Singapore English, Hong Kong English, Brunei English; but American English, German English, Malaysian English. On this basis, we might favour Chinese English over China English, because China is big.

Small talk across Englishes: A focus on Namibia

The present paper examines the use of English in Namibia focussed on one particular type of social situation, namely small talk with a stranger at a party. The analysis concentrates on the opening turns-at-talk and contrasts Namibian English to previously analysed native and learner varieties of the English language, specifically English English, Irish English, American English, Canadian English, and German English. This paper thus contributes to research in variational pragmatics comparing national varieties of the same language. More particularly, the present study reveals a range of variety-preferential patterns for Namibian English and thus contributes to establishing a pragmatic profile of English in Namibia.

ERP evidence for Slavic and German word stress cue sensitivity in English.

Word stress is demanding for non-native learners of English, partly because speakers from different backgrounds weight perceptual cues to stress like pitch, intensity, and duration differently. Slavic learners of English and particularly those with a fixed stress language background like Czech and Polish have been shown to be less sensitive to stress in their native and non-native languages. In contrast, German English learners are rarely discussed in a word stress context. A comparison of these varieties can reveal differences in the foreign language processing of speakers from two language families. We use electroencephalography (EEG) to explore group differences in word stress cue perception between Slavic and German learners of English. Slavic and German advanced English speakers were examined in passive multi-feature oddball experiments, where they were exposed to the word impact as an unstressed standard and as deviants stressed on the first or second syllable through higher pitch, intensity, or duration. The results revealed a robust Mismatch Negativity (MMN) component of the event-related potential (ERP) in both language groups in response to all conditions, demonstrating sensitivity to stress changes in a non-native language. While both groups showed higher MMN responses to stress changes to the second than the first syllable, this effect was more pronounced for German than for Slavic participants. Such group differences in non-native English word stress perception from the current and previous studies are argued to speak in favor of customizable language technologies and diversified English curricula compensating for non-native perceptual variation.

Approximating to the Real Translation Quality for Neural Machine Translation via Causal Motivated Methods

It is hard to evaluate translations objectively and accurately, which limits the applications of machine translation. In this article, we assume that the above phenomenon is caused by noise interference during translation evaluation, and we handle the problem through a perspective of causal inference. We assume that the observable translation score is affected by the unobservable true translation quality and some noise simultaneously. If there is a variable that is related to the noise and independent to the true translation quality, the related noise can be eliminated by removing the effect of that variable from the observed score. Based on the above causality hypothesis, this article studies the length bias problem of beam search for neural machine translation (NMT) and the input related noise problem of translation quality estimation (QE). For the NMT length bias problem, we conduct the experiments on four typical NMT tasks (Uyghur–Chinese, Chinese–English, English–German, and English–French) with different scales of datasets. Comparing with previous approaches, the proposed causal motivated method is model-agnostic and does not require supervised training. For QE tasks, we conduct the experiments on the WMT’20 submissions. Experimental results show that the denoised QE results gain better Pearson’s correlation scores with human assessed scores compared to the original submissions. Further analyses on the NMT and QE tasks also demonstrate the rationality of the empirical assumptions made on our methods.

Russian Particle Prosto (‘Simply’): the Expantion of Functions

The lexical means of marking expressiveness: modal particles, adverbials are analyzed. The Russian particle/adverb prosto ‘simply’ is highlighted but German English and Polish equivalents are also taken into consideration. The case of metaphorization based on the concept ‘simple’ is under investigation. In other words this paper intends to make input into the answer to the question: ‘What are the features of lexis that make it possible of figurative thinking in the sphere of expressivity? The idea of simplicity is rather common for means expressing emphasizing and implication in some (not all anyway) languages: Rus. Prosto Fr. (tout) simplement, Germ. einfach Eng. Simply The metaphorical shift in the situation of emphasizing X prosto Y can be reconstructed in this way: ‘there are many ways of evaluating X, X can be named Y and one does not need other, more complicated means’. The meaning of causality (as it often occurs with particles) is mostly based on implicated cause, and that makes it possible to use this particle even when the causal relation is not evident. Rather often the idea of the cause needs a chain of implication: Mozhno? Ya prosto xotela sprosit; May I come in? I just wished to ask’(3). ‘I ask the permission to enter, (because I need something here), and this is to ask for something that I find not complicated,(so the cause of my ask for the permition is a simple desire to get some information)”. In such examples the function of the particle is similar to adverbials (as actually) or even hesitators. The function of hesitators is being developed later than others. This fact can be described in terms of microdiachrony. The conclusion about the importance of describing the system of connections in the language, reflecting the specifics of changing the prototypic meaning in different languages is made.

Intentional and Incidental Vocabulary Learning: The Role of Historical Linguistics in the Second Language Classroom

AbstractAlthough English and German are both Germanic languages, due to various historical changes, many of their cognates are no longer easily recognizable. This study examined whether knowledge of language history can be beneficial to learners when learning English–German cognates. Thirty‐five English‐speaking second language (L2) learners of 3rd‐semester German at an American university were assigned to either an intentional or incidental learning condition. The intentional group received explicit instruction on 2 historical sounds shifts (Second Germanic Sound Shift, Ingvaeonic Palatalization) and relevant historical semantic changes. In contrast, the incidental group carried out various activities that exposed learners to cognates through reading and oral communication tasks. Results indicate that the intentional group significantly outperformed the incidental group, suggesting that knowledge of language history can be beneficial to learners when learning historically related languages. Declarative knowledge of the historical changes led to significantly greater vocabulary gains and it also provided learners with a tool kit to correctly predict the meaning of several cognates they had not previously encountered before. This study has broad implications for vocabulary learning, language teaching, and applied historical linguistics.

Traceback and Chunk-Based Learning: Comparing Usage-Based Computational Approaches to Child Code-Mixing

Recent years have seen increased interest in code-mixing from a usage-based perspective. In usage-based approaches to monolingual language acquisition, a number of methods have been developed that allow for detecting patterns from usage data. In this paper, we evaluate two of those methods with regard to their performance when applied to code-mixing data: the traceback method, as well as the chunk-based learner model. Both methods make it possible to automatically detect patterns in speech data. In doing so, however, they place different theoretical emphases: while traceback focuses on frame-and-slot patterns, chunk-based learner focuses on chunking processes. Both methods are applied to the code-mixing of a German–English bilingual child between the ages of 2;3 and 3;11. Advantages and disadvantages of both methods will be discussed, and the results will be interpreted against the background of usage-based approaches.

The influence of semantic bias on triple non-identical cognates during reading: Evidence from trilinguals’ eye movements

The current study investigated how the processing of triple cognates (words sharing form and meaning across three languages) is modulated by the semantic bias of sentence context in a reading task. In the study, Polish–German–English trilinguals read English sentences while their eye movements were monitored. The sentences were either semantically biased (high-context) or neutral (low-context) towards target words. The targets were either Polish–German–English cognates whose cross-language form overlap was incomplete (e.g. DIAMENT–DIAMANT–DIAMOND) or English-only controls (e.g. KURCZAK–HÄHNCHEN–CHICKEN). The results revealed a significant effect of context in gaze durations and in total reading time. Importantly, no cognate facilitation effect was identified in any reading measure. The gaze duration data additionally revealed that English-only controls were read slower in low-context sentences than in high-context sentences but gaze durations for cognates were not affected by the sentence context. Thus, prior bilingual findings were only partially replicated in the current study with trilinguals. This suggests that bilingual models of language processing should be carefully adapted to trilinguals. The current data may also mean that non-identical cognates (even those shared across three languages) induce relatively small effects and large samples of participants and items may be needed to detect such effects across reading measures.

Cross-language activation during word recognition in child second-language learners and the role of executive function

We investigated lexical retrieval processes in 4- to 6-year-old German–English bilinguals by exploring cross-language activation during second-language (L2) word recognition of cognates and noncognates in semantically related and unrelated contexts in young learners of English. Both button presses (reaction times and accuracies) and eye-tracking data (percentage looks to target) yielded a significant cognate facilitation effect, indicating that the children’s performance was boosted by cognate words. Nonetheless, the degree of phonological overlap of cognates did not modulate their performance. Moreover, a semantic interference effect was found in the children’s eye movement data. However, in these young L2 learners, cognate status exerted a comparatively stronger impact on L2 word recognition than semantic relatedness. Finally, correlational analyses on the cognate and noncognate performance and the children’s executive function yielded a significant positive correlation between noncognate performance and their inhibitory control, suggesting that noncognate processing depended to a greater extent on inhibitory control than cognate processing.

X-Transformer: A Machine Translation Model Enhanced by the Self-Attention Mechanism

Machine translation has received significant attention in the field of natural language processing not only because of its challenges but also due to the translation needs that arise in the daily life of modern people. In this study, we design a new machine translation model named X-Transformer, which refines the original Transformer model regarding three aspects. First, the model parameter of the encoder is compressed. Second, the encoder structure is modified by adopting two layers of the self-attention mechanism consecutively and reducing the point-wise feed forward layer to help the model understand the semantic structure of sentences precisely. Third, we streamline the decoder model size, while maintaining the accuracy. Through experiments, we demonstrate that having a large number of decoder layers not only affects the performance of the translation model but also increases the inference time. The X-Transformer reaches the state-of-the-art result of 46.63 and 55.63 points in the BiLingual Evaluation Understudy (BLEU) metric of the World Machine Translation (WMT), from 2014, using the English–German and English–French translation corpora, thus outperforming the Transformer model with 19 and 18 BLEU points, respectively. The X-Transformer significantly reduces the training time to only 1/3 times that of the Transformer. In addition, the heat maps of the X-Transformer reach token-level precision (i.e., token-to-token attention), while the Transformer model remains at the sentence level (i.e., token-to-sentence attention).

GECE VE GÜNDÜZ SÖZCÜKLERİNDE ARDIŞIK DÜZEN

Humans have been waiting for the enlightenment of darkness for a long time. They also tried to be a light to the night when the illumination, that is, the end of the night and the beginning of the day, was not enough. The concepts of night and day have found their place as words in every language and literature. Gece ve gündüz in Turkish, daytime and night in English, Tag und Nacht in German, Den' i noch' (День и ночь) in Russian, leyl and nehâr (ليل نهار ) in Arabic, rûz and şeb (روز و شب) in Persian. The concepts of night and day have started to be processed in Turkish language culture since our first written works. In Kokturk inscriptions, Bilge Kağan's " Öd tengri yaşar kişi oglı kop ölgeli törümiş." That is, “God lives in time, human beings are always descended/created to die.” The limited framework of the concept of time, whose philosophy has been revealed in the sentence, reveals the words tün and kün, that is, night and day. In other words, tün and kün have shown themselves in proverbs, stories, tales, poems, folk songs and many other oral/written genres for centuries day and night. In the Turkish language and culture, night has always found its place first, and the day has been the follower and illuminator of the night. However, this situation may vary in different languages and cultures. Keywords: time, day and night, semantics, concept science

Learning to refine source representations for neural machine translation

Machine translation is one of the most classic application technologies in artificial intelligence and natural language processing. Neural machine translation models generally adopt an encoder–decoder architecture for modeling the entire translation process. However, without considering target context (e.g., decoding state) to guide the encoding, encoded source representations struggle to put great emphasis on important information for predicting some target word, yielding the weakness in generating more discriminative attentive representations across different decoding steps. Towards tackling this issue, we propose a novel encoder–refiner–decoder framework, which dynamically refines the source representations based on the generated target-side information at each decoding step. Since the refining operations are time-consuming, we propose a policy network to decide when to refine at specific decoding steps. We solve such a problem using the Gumbel-Softmax reparameterization, which makes our network differentiable and trainable through standard stochastic gradient methods. Experimental results on both Chinese–English and English–German translation tasks show that the proposed approach significantly and consistently improves translation performance over the standard encoder–decoder framework. Furthermore, when refining strategy is applied, experimental results still show a reasonable improvement over the baseline with much decrease in decoding speed.