This study tested whether native Chinese (L1) readers whose second language (L2) was English could activate L2 translations of L1 words during L1 sentence reading. Chinese–English bilinguals read Chinese sentences silently, each containing a target word whose parafoveal preview was manipulated. To test cross-language semantic activation, each target word was paired with an identical, an unrelated and a translation-related preview that shared an L2 translation (e.g., 政黨, party as a political group) with the target word (e.g., 派對, party as a social gathering). Compared to the unrelated previews, the translation-related previews induced shorter target-word viewing times, despite no phonological/orthographic overlap. Furthermore, the highly proficient L2 readers showed earlier priming effects than did the average readers. Our results suggest that bilinguals activate lexical representations in both languages automatically and non-selectively, even when the task requires activation of one language only, and that the L2 lexical activation is modulated by L2 proficiency.

This study examined the variability of language profiles in Spanish–English bilingual children with and without developmental language disorder (DLD). The data included 529 children between the ages of 5 and 10 years. Eighty-eight of these children were identified as having DLD. A latent profile analysis was conducted based on children’s morphosyntax and semantics performance in Spanish and English. The optimal model identified five different profiles, illustrating the heterogeneity in bilingual development. Children with DLD were observed across all profiles, but most were classified in the only two profiles where lower morphosyntax than semantic performance was observed across languages. These results show the variability in both bilingual children with and without DLD. Additionally, the hallmark deficit of DLD in morphosyntax was confirmed, with the morphological weakness being observed in each of the bilingual children’s languages. Children’s background factors (age, maternal education and language exposure) were associated with profile characteristics.

How do children learn the language-to-concept mappings within the domain of Mechanical Support – a spatial domain involving varied and complex force-dynamic relations between objects based on specific mechanisms (stickiness, clips, etc.)? We explore how four- and six-year-olds, and adults encode dynamic events and static configurations of Mechanical Support via attachment (picture put on a door). Participants viewed spatial configurations (Experiment 1 – in dynamic events or Experiment 2 – in static states) and were then prompted with the question, “Can you tell me what my sister did with my toy?” Children and adults used lexical verbs, and the visibility of the mechanism influenced the type of verb used. Also, whereas children preferentially used Orientation Verbs (e.g., “hang”), adults preferentially used Verbs of Attaching (e.g., “tape,” “stick”). Our findings shed light on how children acquire mechanical support language and the linguistic and cognitive constraints involved.

This study investigated whether nonnative speakers of English would be able to identify the meanings of ambiguous English noun-noun compounds, focusing on semantic relation between the modifier and head. English noun-noun compounds with varying degrees of ambiguity were selected through an analysis of contexts from COCA. The participants were two groups of college students, who were natives peakers of Arabic (n = 20) and Chinese (n = 20). The participants thought-aloud the meanings (more common vs. less common) of the English noun-noun compounds shown in the contexts. The overall accuracy was comparable between the groups, but by-item accuracy showed some differences in the meanings the groups identified more accurately.

Designing effective language learning settings requires an understanding of the processes taking place in language learning and the way they interact. One important issue concerns the interaction between meaning and grammar. A number of studies have shown a beneficial effect of semantics in grammar learning. What is unclear, however, is how far this effect may be influenced by the presentation formats of the semantic content. In two experiments, participants performed rule search tasks on Latin sentences. In Experiment 1, we presented semantic information in the form of naturalistic photographs, whereas in Experiment 2, the semantic information was implemented by quasi-translations. The control groups did not receive any semantic information. Learning performance was assessed by a grammaticality-judgment task combined with a source-attributions task. In both experiments, participants in the with-semantics group outperformed the respective control groups. Yet, only in Experiment 1, participants report having more explicit than implicit knowledge. We argue that semantic information boosts the acquisition of grammatical structures regardless of the presentation format. Furthermore, we suggest that, consistent with multimedia learning theories, the pictorial presentation format of Experiment 1 helped to use working memory capacity efficiently, which may have led to the generation of more explicit knowledge.

Transformer-based neural language models achieve state-of-the-art performance on various natural language processing tasks. However, an open question is the extent to which these models rely on word-order/syntactic or word co-occurrence/topic-based information when processing natural language. This work contributes to this debate by addressing the question of whether these models primarily use topic as a signal, by exploring the relationship between Transformer-based models’ (BERT and RoBERTa’s) performance on a range of probing tasks in English, from simple lexical tasks such as sentence length prediction to complex semantic tasks such as idiom token identification, and the sensitivity of these tasks to the topic information. To this end, we propose a novel probing method which we call topic-aware probing. Our initial results indicate that Transformer-based models encode both topic and non-topic information in their intermediate layers, but also that the facility of these models to distinguish idiomatic usage is primarily based on their ability to identify and encode topic. Furthermore, our analysis of these models’ performance on other standard probing tasks suggests that tasks that are relatively insensitive to the topic information are also tasks that are relatively difficult for these models.

Using data from the understudied language Gιsιɖa Anii, we provide a formal analysis of irrealis that builds on the framework of modality proposed in Giannakidou and Mari. In particular, we propose that Anii has an irrealis modal morpheme whose meaning is that the speaker does not believe that the proposition is true at a particular time. This gives irrealis, at least in Anii, a negatively biased meaning. Giannakidou and Mari propose that the subjunctive in European languages is a positively biased modal but find no evidence in their data for a corresponding negatively biased one. However, in expanding their approach to a completely unrelated language, we show that modal bias can also be negative, filling in the paradigmatic gap left open by Giannakidou and Mari’s work. We also illustrate the utility of analyzing irrealis (in relation to the concept of veridicality) as a morphosyntactic and semantic category with a status similar to tense and aspect. Our formal analysis accounts for the obligatory realization of irrealis in a wide range of semantic contexts in Anii, including future tense, negation, and wishes, and shows how irrealis can be composed with other clausal elements. We suggest that reality status, which we analyze as (non)-veridicality, is obligatorily present in the Anii clause and discuss the implications of this for other languages.

I argue that more and less are always determinatives, contrary to the categorization in The Cambridge Grammar of the English Language (CGEL), which treats them as adverbs in analytic comparatives. Evidence is presented of contrasts between more/less and much/little in various contexts, challenging CGEL’s empirical claim that such contrasts never occur in analytic comparatives. The observed distributional patterns can largely be explained by the semantics of more/-er and much without positing a category distinction: more/-er establishes a salient scale-internal reference point, while much requires such a point to already be present. Furthermore, mere distributional differences should not be relied upon for category assignment, following arguments by Payne , Huddleston & Pullum (2010). For these reasons, analyzing more and less as adverbs in any context is unnecessary and unparsimonious. The determinative analysis can account for all the relevant data. Beyond the narrow point about categorization, the paper illustrates the contributions of semantic, pragmatic, and distributional evidence in resolving category assignment.

We investigate a class of adjective phrases composed of a deadjectival adverb ending in -ly and an adjective head (e.g. staggeringly incompetent, absolutely terrific, fiscally responsible), a compact construction whereby two adjectives may jointly contribute to evaluative meaning. Using corpus methodologies on more than 1 million examples and relying on semantic analyses of about 1,000 instances, we propose that the construction can be divided into different semantic subtypes, including Degree (deeply disturbing), Focus (utterly ridiculous), Manner (delightfully performed), Reaction (strangely compelling), Topical (historically inaccurate) and Epistemic (intuitively obvious), among others. Using this typology, we investigate the relative distribution of each subtype across several registers of written English. We found a high frequency of the Reaction subtype in book, film and art reviews, and we suggest a discourse-functional explanation for this, linked to the perceived value of originality in expressive writing. This investigation reveals the power of semantically informed, corpus methodologies to shed light on the distribution of specific constructions.