We propose the Neuro-Symbolic Concept Learner (NS-CL), a model that learns visual concepts, words, and semantic parsing of sentences without explicit supervision on any of them; instead, our model learns by simply looking at images and reading paired questions and answers. Our model builds an object-based scene representation and translates sentences into executable, symbolic programs. To bridge the learning of two modules, we use a neuro-symbolic reasoning module that executes these programs on the latent scene representation. Analogical to human concept learning, the perception module learns visual concepts based on the language description of the object being referred to. Meanwhile, the learned visual concepts facilitate learning new words and parsing new sentences. We use curriculum learning to guide the searching over the large compositional space of images and language. Extensive experiments demonstrate the accuracy and efficiency of our model on learning visual concepts, word representations, and semantic parsing of sentences. Further, our method allows easy generalization to new object attributes, compositions, language concepts, scenes and questions, and even new program domains. It also empowers applications including visual question answering and bidirectional image-text retrieval.
Abstract Habitat‐selection analyses allow researchers to link animals to their environment via habitat‐selection or step‐selection functions, and are commonly used to address questions related to wildlife management and conservation efforts. Habitat‐selection analyses that incorporate movement characteristics, referred to as integrated step‐selection analyses, are particularly appealing because they allow modelling of both movement and habitat‐selection processes. Despite their popularity, many users struggle with interpreting parameters in habitat‐selection and step‐selection functions. Integrated step‐selection analyses also require several additional steps to translate model parameters into a full‐fledged movement model, and the mathematics supporting this approach can be challenging for many to understand. Using simple examples, we demonstrate how weighted distribution theory and the inhomogeneous Poisson point process can facilitate parameter interpretation in habitat‐selection analyses. Furthermore, we provide a ‘how to’ guide illustrating the steps required to implement integrated step‐selection analyses using the amt package By providing clear examples with open‐source code, we hope to make habitat‐selection analyses more understandable and accessible to end users.
Marta Kajzer-Wietrzny, I. Ivaska, Adriano Ferraresi
There is general consensus among interpreting practitioners and scholars that numbers pose particular problems in simultaneous interpreting. Adopting the view that fluency disruptions in interpreters’ renditions are signals of cognitive processing problems, the authors aim to isolate those contextual and textual factors which increase the likelihood of disfluencies when rendering numbers present in a source speech. In the reported study, we analyse data from the European Parliament Translation and Interpreting Corpus (EPTIC): we focus on target-text segments whose corresponding source segment contains a number and we find the best predictors of disfluencies by applying a generalized linear mixed model. Our approach is confirmatory and so the model accounts for factors that have been suggested in earlier studies as being associated with interpreting fluency. These factors include the nativeness of the original speaker, the type of number, the frequency of numbers in the same sentence, omission, language pair and whether the text was originally delivered impromptu or read out, and at what pace. The outcomes suggest that important predictors of disfluent renditions include omission, the frequency of numbers in a sentence and the type of number; these can be said to contribute to interpreters’ cognitive load when they process numbers.
The translation of references specific to a given source culture has long been a prominent, and often problematic aspect of translation practice and research. In indirect translation, or the translation of already translated material, linguistic and cultural differences accumulate, meaning that the omission of cultural references (CRs) or culture-specific items (CSIs) might be a generally expected outcome. Yet before such hypotheses can be tested, research methods are needed that can account for broad patterns across whole texts, and preferably, across semantic categories, genres, time periods, and languages. A ‘textual’ approach, focused on the linguistic context in which CRs are likely to occur, should complement the currently dominant ‘cultural’ approach, which mainly relies on predefined categories and intuition for the selection of objects of study. This article illustrates that corpus research, and particularly keyness analysis, can aid in uncovering recurrent structural patterns and textual functions in which CRs are expected to pose translation difficulties. In this regard, it focuses on expressions of enumeration, or lists, and indicators of identification, or voice. Based on a trilingual (English, French, and Italian) corpus-assisted study of Jonathan Swift’s Gulliver’s Travels (1726) and John Cary’s An Essay on the State of England (1695), the article accentuates the productive complementarity of numerical operations and context-sensitive readings.
Language and Literature, Language. Linguistic theory. Comparative grammar
Since the beginning of 2020, the COVID-19 pandemic has forced universities around the world to engage quickly and efficiently with online teaching platforms. Yet even before the pandemic hit, many programmes had been addressing challenges related to globalisation and technologisation within the teaching and learning context by moving to online or blended teaching and learning modes. In both the immediate pre- and post-pandemic context, movement towards the use of innovative technologies such as virtual reality (VR) to enhance the student experience have occurred in disciplines that heavily rely on practice-based learning (such as the health sciences and psychology). This paper describes an innovative approach to community interpreter training, which is in high demand in Australia. The VR project under examination here aims to provide evidence-based, pedagogically-sound, authentic, situated learning scenarios in a safe, virtual environment so that students are better prepared to deal with the complexities of the role of an interpreter in family violence (FV) settings. Using the VR platform, trainees will be given the opportunity to engage in simulated interpreting tasks working with victims of FV, social workers, police and other field-specific protagonists. In this article, we outline the methodology applied to the provision of interpreter training in this specific VR context. This methodology will serve as a blueprint for other institutions — particularly those offering specialised interpreter training — looking to minimise the threat to face-to-face contexts introduced by the pandemic, but also eager to expand into more experiential teaching offerings that reach beyond traditional modes used for interpreter training.