Transformational Grammar Research Articles

Blending BDI Agents with Object-Oriented and Functional Programming with JaKtA

The popularity of multi-paradigm languages is on the rise, enabling developers to select the most appropriate paradigm for each task. While object-oriented and functional programming are commonly combined, other paradigms can also be hybridized. This paper introduces JaKtA, an internal Domain-Specific Language designed to support the definition of Belief-Desire-Intention agents in Kotlin. Our work represents an initial exploration into blending Agent-Oriented Programming with other prevalent paradigms, emphasizing the potential benefits of using internal Domain-Specific Languages. We demonstrate, through JaKtA, how this approach facilitates the creation of compact and expressive Belief-Desire-Intention agents that seamlessly integrate with the host language, its libraries, and tooling.

The Linguistic Ecologic Analysis of the Error-prone Emergency Prevention and Control Slogans during the COVID-19 Epidemic in China

As a typical form of emergency language service, the emergency prevention and control slogans (EPCS) play an important role in the prevention and control of social emergencies in China. However, due to the characteristics of "emergency", the prevention and control slogans are errorprone, which can cause a negative impact on the language ecology. Based on the theory of linguistic ecology, this paper conducts a thorough analysis of the error-prone slogans during the COVID-19 epidemic in China. First, error-prone slogans can be divided into four categories from the internal language ecological environment: language standardization errors, application appropriateness errors, content adaptability errors, and ethical compliance errors. Second, this paper assesses three aspects of error-prone slogans from external social ecological environment: the degree of errors, the distribution of errors, and the citizens' normative willingness. Finally, the paper puts forward suggestions to prevent language conflict, stop language exclusion, eliminate language dissent and govern the language environment in order to reduce error-prone emergency prevention and control slogans, improve the quality of emergency language services, and build a harmonious ecological environment inside and outside the language.

Research and development of automation subroutine for forecasting the number and load of cargo vehicles depending on the stochastic formation of dangerous goods collection centers

The issues of forecasting the load of vehicles, route networks, formation of collection centers for cargo flows, including dangerous goods, in territorial planning systems determine the need for transition from deterministic models to dynamic models with the possibility of taking into account the influence of the external environment. Existing information transportation systems in the sphere of work with dangerous goods are based on deterministic models and allow to study the problems of forecasting changes to a limited extent. Models of routing networks construction on cargo vehicles are limitedly applicable when there is a task of dangerous goods transportation taking into account the presence of a distributed system of accumulation centers both in the city and, for example, within a large transport hub, seaport with different terminals. The paper presents routing models adapted to the conditions of dangerous goods transportation. Determining transport efficiency and the main costs of transport enterprises is the scheme of traffic flows, fixed in the territorial scheme. The task of the study is to develop a separate automation toolkit for decision making on the number and type of specialized transport (depending on the load capacity) to work with dangerous goods and large-sized objects within the existing flow scheme. On the basis of the research of the group of companies the conclusion is presented that today the task of choosing the optimal amount of transport by carriers is solved, as a rule, not systematically, but by operative construction of a set of routes, and does not involve primary analysis of the territory and parameters of accumulation sites. The presented study considers a new approach to the analysis of the system of transportation of hazardous waste, associated large cargoes, in terms of assessing the necessary list of universal structured input data, which are primary principled and characterize the complexity of work in the selected facility, terminal. To solve the problem of forecasting the load of vehicles is offered developed a new automation subprogram based on the organization of client-server access to data, and implemented in the internal programming language “1C. Management of motor transport” (version ММT 8.2). The presented automation subprogram provides data connectivity on volumes of dangerous goods cargo flows, cargo flow collection centers, available data on vehicles and planned data on removal schedules. Work of the subprogram provides integration of work with other databases of transport companies and allows to carry out modeling of various variants of placement of centers of collection of dangerous goods, centers of cargo flows with high accuracy. The obtained results allow to determine the required number of vehicles, their load taking into account different types and technical capabilities, as well as to determine the required number of vehicle trips in the serviced area taking into account the dynamic influence of the external environment. The presented methodology of working with data, performed in a separate automation subprogram, presented routing models, adapted to the transportation of dangerous goods, can be applied to various transport systems, including sea cargo ports and terminals, which determines the universality of the presented approach.

Automated Verification of Higher-Order Probabilistic Programs via a Dependent Refinement Type System

Verification of higher-order probabilistic programs is a challenging problem. We present a verification method that supports several quantitative properties of higher-order probabilistic programs. Usually, extending verification methods to handle the quantitative aspects of probabilistic programs often entails extensive modifications to existing tools, reducing compatibility with advanced techniques developed for qualitative verification. In contrast, our approach necessitates only small amounts of modification, facilitating the reuse of existing techniques and implementations. On the theoretical side, we propose a dependent refinement type system for a generalised higher-order fixed point logic (HFL). Combined with continuation-passing style encodings of properties into HFL, our dependent refinement type system enables reasoning about several quantitative properties, including weakest pre-expectations, expected costs, moments of cost, and conditional weakest pre-expectations for higher-order probabilistic programs with continuous distributions and conditioning. The soundness of our approach is proved in a general setting using a framework of categorical semantics so that we don’t have to repeat similar proofs for each individual problem. On the empirical side, we implement a type checker for our dependent refinement type system that reduces the problem of type checking to constraint solving. We introduce admissible predicate variables and integrable predicate variables to constrained Horn clauses (CHC) so that we can soundly reason about the least fixed points and samplings from probability distributions. Our implementation demonstrates that existing CHC solvers developed for non-probabilistic programs can be extended to a solver for the extended CHC with only small efforts. We also demonstrate the ability of our type checker to verify various concrete examples.

How to Bake a Quantum Π

We construct a computationally universal quantum programming language Quantum Π from two copies of Π, the internal language of rig groupoids. The first step constructs a pure (measurement-free) term language by interpreting each copy of Π in a generalisation of the category Unitary in which every morphism is “rotated” by a particular angle, and the two copies are amalgamated using a free categorical construction expressed as a computational effect. The amalgamated language only exhibits quantum behaviour for specific values of the rotation angles, a property which is enforced by imposing a small number of equations on the resulting category. The second step in the construction introduces measurements by layering an additional computational effect.

Double-Ended Bit-Stealing for Algebraic Data Types

Algebraic data types are central to the development and evaluation of most functional programs. It is therefore important for compilers to choose compact and efficient representations of such types, in particular to achieve good memory footprints for applications. Algebraic data types are most often represented using blocks of memory where the first word is used as a so-called tag, carrying information about the constructor, and the following words are used for carrying the constructor's arguments. As an optimisation, lists are usually represented more compactly, using a technique called bit-stealing, which, in its simplest form, uses the word-alignment property of pointers to byte-addressed allocated memory to discriminate between the nil constructor (often represented as 0x1) and the cons constructor (aligned pointer to allocated pair). Because the representation supports that all values can be held uniformly in one machine word, possibly pointing to blocks of memory, type erasure is upheld. However, on today's 64-bit architectures, memory addresses (pointers) are represented using only a subset of the 64 bits available in a machine word, which leave many bits unused. In this paper, we explore the use, not only of the least-significant bits of pointers, but also of the most-significant bits, for representing algebraic data types in a full ML compiler. It turns out that, with such a particular utilisation of otherwise unused bits, which we call double-ended bit-stealing, it is possible to choose unboxed representations for a large set of data types, while still not violating the principle of uniform data-representations. Examples include Patricia trees, union-find data structures, stream data types, internal language representations for types and expressions, and mutually recursive ASTs for full language definitions. The double-ended bit-stealing technique is implemented in the MLKit compiler and speedup ranges from 0 to 26 percent on benchmarks that are influenced by the technique. For MLKit, which uses abstract data types extensively, compilation speedups of around 9 percent are achieved for compiling MLton (another Standard ML compiler) and for compiling MLKit itself.

Game Comonads & Generalised Quantifiers

Game comonads, introduced by Abramsky, Dawar and Wang and developed by Abramsky and Shah, give an interesting categorical semantics to some Spoiler-Duplicator games that are common in finite model theory. In particular they expose connections between one-sided and two-sided games, and parameters such as treewidth and treedepth and corresponding notions of decomposition. In the present paper, we expand the realm of game comonads to logics with generalised quantifiers. In particular, we introduce a comonad graded by two parameters $n \leq k$ such that isomorphisms in the resulting Kleisli category are exactly Duplicator winning strategies in Hella's $n$-bijection game with $k$ pebbles. We define a one-sided version of this game which allows us to provide a categorical semantics for a number of logics with generalised quantifiers. We also give a novel notion of tree decomposition that emerges from the construction.

Decoupled structure for improved adaptability of end-to-end models

Although end-to-end (E2E) trainable automatic speech recognition (ASR) has shown great success by jointly learning acoustic and linguistic information, it still suffers from the effect of domain shifts, thus limiting potential applications. The E2E ASR model implicitly learns an internal language model (LM) which characterises the training distribution of the source domain, and the E2E trainable nature makes the internal LM difficult to adapt to the target domain with text-only data. To solve this problem, this paper proposes decoupled structures for attention-based encoder–decoder (Decoupled-AED) and neural transducer (Decoupled-Transducer) models, which can achieve flexible domain adaptation in both offline and online scenarios while maintaining robust intra-domain performance. To this end, the acoustic and linguistic parts of the E2E model decoder (or prediction network) are decoupled, making the linguistic component (i.e. internal LM) replaceable. When encountering a domain shift, the internal LM can be directly replaced during inference by a target-domain LM, without re-training or using domain-specific paired speech-text data. Experiments for E2E ASR models trained on the LibriSpeech-100h corpus showed that the proposed decoupled structure gave 15.1% and 17.2% relative word error rate reductions on the TED-LIUM 2 and AESRC2020 corpora while still maintaining performance on intra-domain data. It is also shown that the decoupled structure can be used to boost cross-domain speech translation quality while retaining the intra-domain performance.

Noam Chomsky's Contribution to Linguistics

This research paper explores Noam Chomsky's groundbreaking contributions to linguistics, focusing on his theories and their impact on our understanding of language acquisition and structure. Chomsky introduced the concept of transformational-generative grammar, revolutionising the study of syntax by proposing that all human languages share a common underlying structure, which he termed universal grammar. His work emphasized the innate nature of language acquisition, challenging behaviourist views and suggesting that humans are born predisposed to learn the language. The paper also examines the critical period hypothesis and how Chomsky's theories have influenced contemporary linguistic research and cognitive science. By delving into Chomsky's extensive work, this study highlights his role as a pioneering figure in modern linguistics and his enduring legacy in the field

The role of ChatGPT readiness in shaping language teachers' language teaching innovation and meeting accountability: A bisymmetric approach

There are some dichotomies surrounding ChatGPT's application and impact on education as a central part of artificial intelligence (AI) integration. Problems concerning negative preconceptions and the limited usage of this form of AI have been reported, as some teachers perceive it as a competitor. This is due to insufficient readiness for its integration and a lack of understanding of how it can facilitate teaching innovation. Due to this, the researchers applied a bisymmetric approach, where necessity conditional analysis (NCA) as a symmetrical approach employs necessity logic to identify the must-have factors required for language teachers' passing accountability, while PLS-SEM as an asymmetrical approach follows additive sufficiency logic to identify the should-have factors that contribute to helping them to pass their accountability. Applying this approach would generate more results from different perspectives in ChatGPT readiness and language teaching innovation with it. In this line, the researchers randomly explored 124 Iranian in-service language teachers ChatGPT readiness and their teaching innovation in helping them pass the external and internal language teaching competencies. The result of the PLS-SEM showed that the more the language teachers were ready to practically use ChatGPT alongside, aware of its opportunities and challenges in English language teaching (ELT), the more they could generate more language teaching methods and approaches with it, apply them to their language teaching procedures, and share them with their colleagues. Moreover, their generational language teaching methods with ChatGPT mediated the correlation between ChatGPT readiness and meeting internal accountability. Additionally, the NCA showed that the generation of language teaching methods with ChatGPT and their implementation through it were among the necessary factors that help language teachers meet both internal and external accountability by teaching language with ChatGPT. Based on these findings, the researchers recommended that stakeholders shift their focus from AI programming to AI teaching, especially for ELT.

Detection and Classification of Lanes Using Reannotated Lane Dataset to Aid Autonomous Driving

The core functionality of advanced driver assistance systems and self-driving cars depends on the ability to recognize drivable road areas. As there are many different categories of useful markings incorporated within the road area, lane detection and classification form a major step for taking appropriate actions leading to truly autonomous driving. Existing datasets do not provide ample classification of lane types and adequate granularity for precise localization of lane markings. A new dedicated dataset for semantic segmentation of 11 varied lane types obtained by reannotating the BDD100K dataset is presented. The reannotation process involves pixel-level lane markings of 76,000 image instances of the BDD100K dataset, which were originally represented as a sequence of coordinate points. This opens up the possibility of high resolution both spatially and semantically in the context of lane understanding for autonomous driving. Baseline results on the proposed dataset based on the Bilateral Segmentation Network (BiSeNetV2), which considers the spatial data and the categorical semantics distinctly, are presented. The performance is pinned at 85% accuracy during testing using BiSeNetV2 architecture. The dataset is expected to open up new directions of research to address problems such as severe class imbalance, segmentation of multiple classes of texture-less and eccentric features (lanes), and so forth. As a result, applications such as lane-centric activity interpretation, future event prediction, and continuous learning are expected.

The Ecology of English Loanwords in Chinese: A Case Study of Cement

From an ecolinguistic perspective, this study focuses on five English loanwords in Chinese related to the field of cement, as defined in the Modern Chinese Dictionary. The research quantitatively examines the ecological dynamics of these loanwords using two indicators: lexical niche breadth and overlap. The goal is to uncover the evolutionary mechanism governing their adaptation. The findings show: (1) The emergence of English loanwords is intricately linked to specific social environments. As new concepts and items are introduced from abroad, the masses initially make new loanwords, which are later standardized by authoritative bodies. (2) The vitality of loanwords correlates with their niche breadth. The competition among lexical variants is influenced by niche overlap. The loanwords for cement, ranked by vitality in descending order, are ShuiNi (3.221), YangHui (2.350), ShuiMenTing (1.385), HongMaoNi (1.202), and ShiMinTu (0.879). (3) The endangerment of loanwords results from a combination of external (social environment) and internal (language system) factors. Intense competition arises due to the presence of multiple synonyms for the same entity, and localization challenges occur when the loanwords do not precisely fit the entity. Among the five loanwords for cement, the first two exhibit higher vitality and continue to develop sustainably, while the last three show lower vitality and are gradually becoming endangered. As these loanwords undergo continuous evolution, a lexical ecocontinuum emerges: extinct in the wild—ShiMinTu; critically endangered—HongMaoNi; endangered—ShuiMenTing; vulnerable—YangHui; least concerned—ShuiNi.

A Psycholinguistic Study of the Language of Under-Five Users of English

This research is a psycholinguistic study of the language of Nigerian children under the age of five years. The purpose of this investigation is to explore the process through which the under-five children acquire and use language including their cognitive development. A review of related materials formed the basis of our procedure. Data was collected from informants through spontaneous speeches, personal interviews and oral tests. Two nursery schools provided the data for this study. They are Delta State University Staff School, Abraka and Awaike Primary School, Abraka. The theoretical framework adopted for this study is Chomsky’s Transformational Generative Grammar. Data collected was grouped and built into tables for analysis based on the hypotheses tested. The results of the analysis revealed that both innate and environmental factors are responsible for the acquisition of language by children under five years of age. The research, therefore, recommends that since language acquisition by children is greatly influenced by adults’ utterances, adults should be mindful of how words are pronounced since children learn by imitation.

Supporting meta-model-based language evolution and rapid prototyping with automated grammar transformation

In model-driven engineering, developing a textual domain-specific language (DSL) involves constructing a meta-model, which defines an underlying abstract syntax, and a grammar, which defines the concrete syntax for the DSL. We consider a scenario in which the meta-model is manually maintained, which is common in various contexts, such as blended modeling, in which several concrete syntaxes co-exist in parallel. Language workbenches such as Xtext support such a scenario, but require the grammar to be manually co-evolved, which is laborious and error-prone. In this paper, we present GrammarTransformer, an approach for transforming generated grammars in the context of meta-model-based language evolution. To reduce the effort for language engineers during rapid prototyping and language evolution, it offers a catalog of configurable grammar transformation rules. Once configured, these rules can be automatically applied and re-applied after future evolution steps, greatly reducing redundant manual effort. In addition, some of the supported transformations can globally change the style of concrete syntax elements, further significantly reducing the effort for manual transformations. The grammar transformation rules were extracted from a comparison of generated and existing, expert-created grammars, based on seven available DSLs. An evaluation based on the seven languages shows GrammarTransformer’s ability to modify Xtext-generated grammars in a way that agrees with manual changes performed by an expert and to support language evolution in an efficient way, with only a minimal need to change existing configurations over time.

A Brief Survey on the Categorical Semantics for Geometry of Interaction In Memory of Phil Scott

Philip Scott, better known to his friends and colleagues as Phil, was a Professor of Mathematics at the University of Ottawa (Canada). Sadly, Phil passed away 1 after a long battle with cancer - which he bravely fought till the very end - on 18 December 2023 at the age of 76. Phil was born on 27 December 1947 in Leeds (UK). When Phil was still a baby, his family moved to North Carolina (USA), where he spent his childhood. He went on to study mathematics at the University of North Carolina at Chapel Hill (USA), before moving to Canada in the 1970s. Phil did his Ph.D. at the University of Waterloo (Canada), supervised by Denis Higgs, and obtained his doctorate in pure mathematics in 1976. Phil joined the Mathematics Department at the University of Ottawa in 1982, eventually becoming Professor Emeritus, and remained there until his passing.

MCN4Rec: Multi-level Collaborative Neural Network for Next Location Recommendation

Next location recommendation plays an important role in various location-based services, yielding great value for both users and service providers. Existing methods usually model temporal dependencies with explicit time intervals or learn representation from customized point of interest (POI) graphs with rich context information to capture the sequential patterns among POIs. However, this problem is perceptibly complex, because various factors, e.g., users’ preferences, spatial locations, time contexts, activity category semantics, and temporal relations, need to be considered together, while most studies lack sufficient consideration of the collaborative signals. Toward this goal, we propose a novel M ulti-Level C ollaborative Neural N etwork for next location Rec ommendation (MCN4Rec). Specifically, we design a multi-level view representation learning with level-wise contrastive learning to collaboratively learn representation from local and global perspectives to capture complex heterogeneous relationships among user, POI, time, and activity categories. Then, a causal encoder-decoder is applied to the learned representations of check-in sequences to recommend the next location. Extensive experiments on four real-world check-in mobility datasets demonstrate that our model significantly outperforms the existing state-of-the-art baselines for the next location recommendation. Ablation study further validates the benefits of the collaboration of the designed sub-modules. The source code is available at https://github.com/quai-mengxiang/MCN4Rec .

Inner Voice in Silent Reading and Speaking

Current research reveals that individuals use two languages: external language and internal language. The external language is a loud, noisy language that everyone can hear. The internal language is a silent, mysterious, personal language that merely the individual hears. This phenomenon, called inner language, inner voice or inner speech, is a mental activity seen in most people. This activity plays important roles in language and cognitive processes, helps the processes such as reading, writing, understanding, questioning, learning, thinking, controlling emotions, motivation, and so on. The inner voice emerges during silent reading and contributes significantly to understanding words, sentences, and text. Expert readers read and understand the words by hearing the small inner sound of the words in their brain. Controversy, internal speech is when an individual talks silently to himself. Inner speech is referred when planning daily tasks, making decisions, solving problems, and leading social and emotional issues. Nonetheless, research on the inner voice and inner speech, which are effective in the development of the individual, is yet insufficient. There is a need for clearer and more comprehensive research on the role and function of inner speech in processes such as reading, speaking, understanding, and thinking. It is recommended that this phenomenon, which exists in every individual, be handled scientifically and used to develop language and cognitive skills.

Internal state language factor structure and development in toddlerhood: Insights from WordBank.

Internal state language (ISL) research contains knowledge gaps, including dimensionality and predictors of growth, addressed here in a two-aim study. Parent-reported expressive language from N = 6,373 monolingual, English-speaking toddlers (Mage = 23.5mos, 46% male, 57% white) was collected using cross-sectional and longitudinal data in WordBank. Exploratory and confirmatory factor analyses suggested a best-fitting one-factor model of ISL. The single-factor model of ISL was then submitted to hierarchical linear modeling to evaluate predictors of ISL development. Age 2 ISL production was predicted by child sex, wherein females outperform males, and maternal education, wherein higher education contributes to higher ISL. Only maternal education emerged as a significant predictor of ISL growth. These results provide support to theory suggesting a unitary construct of ISL, as opposed to considering ISL as categorical, and further illustrate linear growth through the second postnatal year that varies as a function of child sex and maternal education.

X-Bartheory: Its Pros/Strengths and Cons/Weaknesses in Generative Grammar

The X-bar theory is an interesting field in linguistic investigation. X-bar theory deepens our understanding of grammatical structure, and as a result may help us use the language in question (English language) more effectively. This study is a critical review, primarily aimed at exploring and examining the strengths and limitations of the X-bar theory in the analysis of syntactic structures in the English language. While we do not claim that X-bar syntax is the “best” approach to the analysis of [English] sentences, it has advantages over some other approaches. The study proves that despite the limitations of the X-bar theory like overworking the I-node et.c. It is an important theory that is worth studying by linguists as relevant in the study of syntactic structures of the English language. The analytical frameworks provided by contemporary scholars, Aarts and Radford, which ultimately are based on insights of Chomsky, Jackendoff and other scholars going back to the 1970s, are used and adapted for the analysis of sentences in this study. These two linguists use the Transformational Generative Grammar approach in their analysis of English phrases and sentences. It is therefore hoped that this study will provide, in addition to the already existing data, a current material on the X-bar theory especially for researchers in the field of generative linguistics.

Nonstandard proof methods in toposes

We determine sufficient structure for an elementary topos to emulate Nelson's Internal Set Theory in its internal language, and show that any topos satisfying the internal axiom of choice occurs as a universe of standard objects and maps. This development allows one to employ the proof methods of nonstandard analysis (transfer, standardisation, and idealisation) in new environments such as toposes of G-sets and Boolean étendues.