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Digital tasks in the foreign language classroom

The sign language proficiency interview

Presentatie op congres The Sign Language Proficiency Interview (SLPI) is a tool for assessing functional sign language skill. Based on the Language Aptitude Test, it uses a recorded 20 minute conversation between a skilled interviewer and the candidate. The interview uses an ad hoc series of probing and challenging questions to elicit the candidate’s best use of the sign language in topics relating to the candidate’s work, family/background, and leisure activities. This video language sample is then analyzed to determine the candidate’s rating on the SLPI Rating Scale. The rating process documents vocabulary, grammar and discourse, and follows a specified protocol that includes specific examples from the interview. The SLPI is used widely in the US and Canada with American Sign Language, and one of the presenters has adapted it for use with South African Sign Language. The presenters have recently adapted the SLPI for use with Sign Language of the Netherlands (NGT). While the interview process is the same regardless of the sign language, two aspects of the adaptation for NGT required work: 1) modifying the grammar analysis to match NGT grammar; and 2) modifying the Rating Scale to align with that of the Common European Framework of Reference for languages (CEFR). This ICED presentation will include: 1) a thorough description of SLPI goals, processes and implementation; 2) modifications for NGT grammar; and 3) modifications to align with the CEFR.

The application of natural language processing for the extraction of mechanistic information in toxicology

To study the ways in which compounds can induce adverse effects, toxicologists have been constructing Adverse Outcome Pathways (AOPs). An AOP can be considered as a pragmatic tool to capture and visualize mechanisms underlying different types of toxicity inflicted by any kind of stressor, and describes the interactions between key entities that lead to the adverse outcome on multiple biological levels of organization. The construction or optimization of an AOP is a labor intensive process, which currently depends on the manual search, collection, reviewing and synthesis of available scientific literature. This process could however be largely facilitated using Natural Language Processing (NLP) to extract information contained in scientific literature in a systematic, objective, and rapid manner that would lead to greater accuracy and reproducibility. This would support researchers to invest their expertise in the substantive assessment of the AOPs by replacing the time spent on evidence gathering by a critical review of the data extracted by NLP. As case examples, we selected two frequent adversities observed in the liver: namely, cholestasis and steatosis denoting accumulation of bile and lipid, respectively. We used deep learning language models to recognize entities of interest in text and establish causal relationships between them. We demonstrate how an NLP pipeline combining Named Entity Recognition and a simple rules-based relationship extraction model helps screen compounds related to liver adversities in the literature, but also extract mechanistic information for how such adversities develop, from the molecular to the organismal level. Finally, we provide some perspectives opened by the recent progress in Large Language Models and how these could be used in the future. We propose this work brings two main contributions: 1) a proof-of-concept that NLP can support the extraction of information from text for modern toxicology and 2) a template open-source model for recognition of toxicological entities and extraction of their relationships. All resources are openly accessible via GitHub (https://github.com/ontox-project/en-tox).

Switching modalities in a sentence verification task : ERP evidence for embodied language processing

In an event related potential (ERP) experiment using written language materials only, we investigated a potential modulation of the N400 by the modality switch effect. The modality switch effect occurs when a first sentence, describing a fact grounded in one modality, is followed by a second sentence describing a second fact grounded in a different modality. For example, "A cellar is dark" (visual), was preceded by either another visual property "Ham is pink" or by a tactile property "A mitten is soft." We also investigated whether the modality switch effect occurs for false sentences ("A cellar is light"). We found that, for true sentences, the ERP at the critical word "dark" elicited a significantly greater frontal, early N400-like effect (270-370 ms) when there was a modality mismatch than when there was a modality-match. This pattern was not found for the critical word "light" in false sentences. Results similar to the frontal negativity were obtained in a late time window (500-700 ms). The obtained ERP effect is similar to one previously obtained for pictures. We conclude that in this paradigm we obtained fast access to conceptual properties for modality-matched pairs, which leads to embodiment effects similar to those previously obtained with pictorial stimuli.

Natural language processing in toxicology: Delineating adverse outcome pathways and guiding the application of new approach methodologies

Adverse Outcome Pathways (AOPs) are conceptual frameworks that tie an initial perturbation (molecular initiat- ing event) to a phenotypic toxicological manifestation (adverse outcome), through a series of steps (key events). They provide therefore a standardized way to map and organize toxicological mechanistic information. As such, AOPs inform on key events underlying toxicity, thus supporting the development of New Approach Methodologies (NAMs), which aim to reduce the use of animal testing for toxicology purposes. However, the establishment of a novel AOP relies on the gathering of multiple streams of evidence and infor- mation, from available literature to knowledge databases. Often, this information is in the form of free text, also called unstructured text, which is not immediately digestible by a computer. This information is thus both tedious and increasingly time-consuming to process manually with the growing volume of data available. The advance- ment of machine learning provides alternative solutions to this challenge. To extract and organize information from relevant sources, it seems valuable to employ deep learning Natural Language Processing techniques. We review here some of the recent progress in the NLP field, and show how these techniques have already demonstrated value in the biomedical and toxicology areas. We also propose an approach to efficiently and reliably extract and combine relevant toxicological information from text. This data can be used to map underlying mechanisms that lead to toxicological effects and start building quantitative models, in particular AOPs, ultimately allowing animal-free human-based hazard and risk assessment.

Empathy matters: ERP evidence for inter-individual differences in social language processing.

When an adult claims he cannot sleep without his teddy bear, people tend to react surprised. Language interpretation is, thus, influenced by social context, such as who the speaker is. The present study reveals inter-individual differences in brain reactivity to social aspects of language. Whereas women showed brain reactivity when stereotype-based inferences about a speaker conflicted with the content of the message, men did not. This sex difference in social information processing can be explained by a specific cognitive trait, one's ability to empathize. Individuals who empathize to a greater degree revealed larger N400 effects (as well as a larger increase in γ-band power) to socially relevant information. These results indicate that individuals with high-empathizing skills are able to rapidly integrate information about the speaker with the content of the message, as they make use of voice-based inferences about the speaker to process language in a top-down manner. Alternatively, individuals with lower empathizing skills did not use information about social stereotypes in implicit sentence comprehension, but rather took a more bottom-up approach to the processing of these social pragmatic sentences.