Multilingual Learning for Mild Cognitive Impairment Screening from a Clinical Speech Task

The Semantic Verbal Fluency Task (SVF) is an efficient and minimally invasive speech-based screening tool for Mild Cognitive Impairment (MCI). In the SVF, testees have to produce as many words for a given semantic category as possible within 60 seconds. State-of-the-art approaches for automatic evaluation of the SVF employ word embeddings to analyze semantic similarities in these word sequences. While these approaches have proven promising in a variety of test languages, the small amount of data available for any given language limits the performance. In this paper, we for the first time investigate multilingual learning approaches for MCI classification from the SVF in order to combat data scarcity. To allow for cross-language generalisation, these approaches either rely on translation to a shared language, or make use of several distinct word embeddings. In evaluations on a multilingual corpus of older French, Dutch, and German participants (Controls=66, MCI=66), we show that our multilingual approaches clearly improve over single-language baselines

Semantic annotation of multilingual learning objects based on a domain ontology

One of the important tasks in the use of learning resources in e-learning is the necessity to annotate learning objects with appropriate metadata. However, annotating resources by hand is time consuming and difficult. Here we explore the problem of automatic extraction of metadata for description of learning resources. First, theoretical constraints for gathering certain types of metadata important for e-learning systems are discussed. Our approach to annotation is then outlined. This is based on a domain ontology, which allows us to annotate learning resources in a language independent way.We are motivated by the fact that the leading providers of learning content in various domains are often spread across countries speaking different languages. As a result, cross-language annotation can facilitate accessibility, sharing and reuse of learning resources

Cross-language Text Classification with Convolutional Neural Networks From Scratch

Cross language classification is an important task in multilingual learning, where documents in different languages often share the same set of categories. The main goal is to reduce the labeling cost of training classification model for each individual language. The novel approach by using Convolutional Neural Networks for multilingual language classification is proposed in this article. It learns representation of knowledge gained from languages. Moreover, current method works for new individual language, which was not used in training. The results of empirical study on large dataset of 21 languages demonstrate robustness and competitiveness of the presented approach

DeMuX: Data-efficient Multilingual Learning

We consider the task of optimally fine-tuning pre-trained multilingual models, given small amounts of unlabelled target data and an annotation budget. In this paper, we introduce DEMUX, a framework that prescribes the exact data-points to label from vast amounts of unlabelled multilingual data, having unknown degrees of overlap with the target set. Unlike most prior works, our end-to-end framework is language-agnostic, accounts for model representations, and supports multilingual target configurations. Our active learning strategies rely upon distance and uncertainty measures to select task-specific neighbors that are most informative to label, given a model. DeMuX outperforms strong baselines in 84% of the test cases, in the zero-shot setting of disjoint source and target language sets (including multilingual target pools), across three models and four tasks. Notably, in low-budget settings (5-100 examples), we observe gains of up to 8-11 F1 points for token-level tasks, and 2-5 F1 for complex tasks. Our code is released here: https://github.com/simran-khanuja/demux

All Languages Welcomed Here

In classrooms across the world, multilingual learning environments help students feel at home and accelerate language learning

Content creation and E-learning in Indian Languages : a model

In the era of E-publishing and E-learning, numerous universities and cultural organizations around the world have launched initiatives to develop tools for multilingual learning and web publishing and have given preference to local content. India has different languages and different culture. Most of the knowledge and information related to people, culture, science and philosophy of India is available in Indian languages, which will be useful for learning and developing knowledge base. In India E-learning systems and online courses are already started, but as a multi lingual country, which gives importance to education through regional languages, there should be facilities for multi lingual E-learning. This paper covers the issues of Indian language knowledge base/content base, its requirement, and its implication in e learning. An Integrated multi lingual E-learning system for India is proposed in this paper, where importance given to multi lingual course content creation

Using vignettes to understand the social-emotional experiences of three-year-olds in diverse language contexts

This article reports on the educational experiences of young Afrikaans mother tongue South African children who are exposed to multilingual learning environments during the preschool years. Vignette research provided observational, co-experiential data of the lived experiences of three-year-old boys as they engaged with formal and informal learning. Vignette data that had been collected through observations, written teacher validation and face-to-face interviews, was controlled against existing literature to provide in-depth insights into the participants’ divergent experiences of and within their learning environment. Findings indicate specific areas in which young children may need additional support in multilingual learning environments, in terms of i) social-emotional security experienced in the learning environment; ii) intentional development of empathy for peers, iii) independence and initiative taking in informal settings, and iv) interactive communication. Although the study focused on South African contexts, its findings may inform future interventions to support multilingual language environments in the early years.&nbsp

Climbing the tower of babel: Unsupervised multilingual learning

For centuries, scholars have explored the deep links among human languages. In this paper, we present a class of probabilistic models that use these links as a form of naturally occurring supervision. These models allow us to substantially improve performance for core text processing tasks, such as morphological segmentation, part-of-speech tagging, and syntactic parsing. Besides these traditional NLP tasks, we also present a multilingual model for the computational decipherment of lost languages