== 12th NLP4CALL, Tórshavn, Faroe Islands==

The workshop series on Natural Language Processing (NLP) for Computer-Assisted Language Learning (NLP4CALL) is a meeting place for researchers working on the integration of Natural Language Processing and Speech Technologies in CALL systems and exploring the theoretical and methodological issues arising in this connection. The latter includes, among others, insights from Second Language Acquisition (SLA) research, on the one hand, and promote development of “Computational SLA” through setting up Second Language research infrastructure(s), on the other.

The intersection of Natural Language Processing (or Language Technology / Computational Linguistics) and Speech Technology with Computer-Assisted Language Learning (CALL) brings “understanding” of language to CALL tools, thus making CALL intelligent. This fact has given the name for this area of research – Intelligent CALL, ICALL. As the definition suggests, apart from having excellent knowledge of Natural Language Processing and/or Speech Technology, ICALL researchers need good insights into second language acquisition theories and practices, as well as knowledge of second language pedagogy and didactics. This workshop invites therefore a wide range of ICALL-relevant research, including studies where NLP-enriched tools are used for testing SLA and pedagogical theories, and vice versa, where SLA theories, pedagogical practices or empirical data are modeled in ICALL tools.

The NLP4CALL workshop series is aimed at bringing together competences from these areas for sharing experiences and brainstorming around the future of the field.

We welcome papers:

- that describe research directly aimed at ICALL;

- that demonstrate actual or discuss the potential use of existing Language and Speech Technologies or resources for language learning;

- that describe the ongoing development of resources and tools with potential usage in ICALL, either directly in interactive applications, or indirectly in materials, application or curriculum development, e.g. learning material generation, assessment of learner texts and responses, individualized learning solutions, provision of feedback;

- that discuss challenges and/or research agenda for ICALL

- that describe empirical studies on language learner data.

This year a special focus is given to work done on error detection/correction and feedback generation.

We encourage paper presentations and software demonstrations describing the above- mentioned themes primarily, but not exclusively, for the Nordic languages.

==Invited speakers==

This year, we have the pleasure to announce two invited talks.

The first talk is given by Marije Michel from the University of Amsterdam.

TELL: Tasks Engaging Language Learners
Taking a task-based approach on language teaching, learning and assessment (TBLT), the basic unit of second language (L2) instruction is a task. Tasks are (pedagogic) activities that adhere to specific criteria (e.g., there needs to be a communicative gap, Skehan, 1998) in order to ensure that learners engage in meaningful language use during task performance. In the long run, only tasks engaging students in authentic language use may lead to L2 processes that have the potential to support L2 acquisition. In this presentation, I will review the most important principles of designing engaging learning tasks, highlight examples of practice-induced L2 research using digital tools, and will showcase some of my own work on task design for L2 learning during digitally mediated communication and L2 writing. In doing so, I will discuss the NLP measures we use to evaluate task-based performance, formulating TBLT desiderata for the future of NLP4CALL.

The second talk is given by Pierre Lison from the Norwegian Computing Center.

Privacy-enhancing NLP: a primer
Text documents often contain personal data in some form – either related to the authors themselves or to some other individuals mentioned in the text. This raises privacy concerns, especially when those documents are to be published online or included as training data for NLP models. For Computer-Assisted Language Learning, this problem is compounded by the presence of various lexical and grammatical errors that may provide additional cues as to the identity of the author. Fortunately, privacy-enhancing techniques can be applied to provide at least some level of privacy, both for the author of the text (or linguistic production) and for the other individuals that may be referred in it. I’ll review in this talk some of those techniques, such as text sanitization, text rewriting, and privacy-preserving training. I’ll also describe in our own work on data-driven text sanitization based on explicit measures of privacy risks and will also present how such methods can be evaluated using our recently released Text Anonymization Benchmark (TAB).

==Important dates==

22 May 2023: workshop date

==Organizers==

David Alfter (1), Elena Volodina (2), Thomas François (3), Arne Jönsson (4), Evelina Rennes (4)

(1) Gothenburg Research Infrastructure in Digital Humanities, Department of Literature, History of Ideas, and Religion, University of Gothenburg, Sweden

(2) Språkbanken Text, Department of Swedish, Multilingualism, Language Technology, University of Gothenburg, Sweden

(3) CENTAL, Institute for Language and Communication, Université Catholique de Louvain, Belgium

(4) Department of Computer and Information Science, Linköping University, Sweden

==Contact==

For any questions, please contact David Alfter, david.alfter@gu.se

For further information, see the workshop website <https://spraakbanken.gu.se/en/research/themes/icall/nlp4call-workshop-series/nlp4call2023>