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Zimianiti, E. (2022). Is semantic memory the winning component in second language teaching with Accelerative Integrated Method (AIM)? LingUU Journal, 6(1), 54-62.
Abstract
This paper constitutes a research proposal based on Rousse-Malpalt’s
(2019) dissertation, which extensively examines the effectiveness of the
Accelerative Integrated Method (AIM) in second language (L2) learning.
Although it has been found that AIM is a greatly effective method in comparison with non-implicit teaching methods, the reasons behind its success and effectiveness are yet unknown. As Semantic Memory (SM) is the component of memory responsible for the conceptualization and storage of knowledge, this paper sets to propose an investigation of its role in the learning process of AIM and provide with insights as to why the embodied experience of learning with AIM is more effective than others. The tasks proposed for administration take into account the factors of gestures being related to a learner’s memorization process and Semantic Memory. Lastly, this paper provides with a future research idea about the learning mechanisms of sign languages in people with hearing deficits and healthy population, aiming to indicate which brain mechanisms benefit from the teaching method of AIM and reveal important brain functions for SLA via AIM.
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Anastasopoulos, A., Lekakou, M., Quer, J., Zimianiti, E., DeBenedetto, J., & Chiang, D. (2018). Part-of-speech tagging on an endangered language: a parallel Griko-Italian Resource. In Proceedings of the 27th International Conference on Computational Linguistics (COLING 2018) (pp. 2529-2539).
Abstract
Most work on part-of-speech (POS) tagging is focused on high resource languages, or examines low-resource and active learning settings through simulated studies. We evaluate POS tagging techniques on an actual endangered language, Griko. We present a resource that contains 114 narratives in Griko, along with sentence-level translations in Italian, and provides gold annotations for the test set. Based on a previously collected small corpus, we investigate several traditional methods, as well as methods that take advantage of monolingual data or project cross-lingual POS tags. We show that the combination of a semi-supervised method with cross-lingual transfer is more appropriate for this extremely challenging setting, with the best tagger achieving an accuracy of 72.9%. With an applied active learning scheme, which we use to collect sentence-level annotations over the test set, we achieve improvements of more than 21 percentage points
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