Publications

Abstract

* These two authors contributed equally to this study
Statistical learning – the ability to extract distributional regularities from input – is suggested to be key to language acquisition. Yet, evidence for the human capacity for statistical learning comes mainly from studies conducted in carefully controlled settings without auditory distraction. While such conditions permit careful examination of learning, they do not reflect the naturalistic language learning experience, which is replete with auditory distraction – including competing talkers. Here, we examine how statistical language learning proceeds in a virtual cocktail party environment, where the to-be-learned input is presented alongside a competing speech stream with its own distributional regularities. During exposure, participants in the Dual Talker group concurrently heard two novel languages, one produced by a female talker and one by a male talker, with each talker virtually positioned at opposite sides of the listener (left/right) using binaural acoustic manipulations. Selective attention was manipulated by instructing participants to attend to only one of the two talkers. At test, participants were asked to distinguish words from part-words for both the attended and the unattended languages. Results indicated that participants’ accuracy was significantly higher for trials from the attended vs. unattended
language. Further, the performance of this Dual Talker group was no different compared to a control group who heard only one language from a single talker (Single Talker group). We thus conclude that statistical learning is modulated by selective attention, being relatively robust against the additional cognitive load provided by competing speech, emphasizing its efficiency in naturalistic language learning situations.

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.

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