Investigating the socio-constructivist dimension of online interactions: the case of synchronous audio-graphic conferencing systems

This study explores the quality of interactive patterns in audio-graphic conferencing environments and learners' involvement in interaction. Supporters of this technology claim that online interactions support socio-constructivist language learning. However, the existing literature does not indicate whether the quality of interaction required for realising constructivist principles of learning can affectively be ensured in such environments. The study is based on the Open University's online audio-graphic tuition environment, Lyceum. It investigates the verbal and written interactions of adult Open University students learning French. The data is analysed by different models of analysis pertaining to different socio-constructivist and cognitive models of analysis. The results show that students use high forms of thinking to engage in a cyclical rather than a linear process of knowledge construction. However, there is no evidence that this process is supported by the audio-graphic system itself. The tutor's style and task design play a more important role in supporting the learning process

All mixed up? Finding the optimal feature set for general readability prediction and its application to English and Dutch

Readability research has a long and rich tradition, but there has been too little focus on general readability prediction without targeting a specific audience or text genre. Moreover, though NLP-inspired research has focused on adding more complex readability features there is still no consensus on which features contribute most to the prediction. In this article, we investigate in close detail the feasibility of constructing a readability prediction system for English and Dutch generic text using supervised machine learning. Based on readability assessments by both experts and a crowd, we implement different types of text characteristics ranging from easy-to-compute superficial text characteristics to features requiring a deep linguistic processing, resulting in ten different feature groups. Both a regression and classification setup are investigated reflecting the two possible readability prediction tasks: scoring individual texts or comparing two texts. We show that going beyond correlation calculations for readability optimization using a wrapper-based genetic algorithm optimization approach is a promising task which provides considerable insights in which feature combinations contribute to the overall readability prediction. Since we also have gold standard information available for those features requiring deep processing we are able to investigate the true upper bound of our Dutch system. Interestingly, we will observe that the performance of our fully-automatic readability prediction pipeline is on par with the pipeline using golden deep syntactic and semantic information

Ludic literacies at the intersections of cultures: an interview with James Paul Gee

Professor James Gee addresses issues of linguistics, literacies and cultures. Gee emphasises the importance of Discourses, and argues that the future of literacy studies lies in the interrogation of new media and the globalisation of culture

Analyzing analytical methods: The case of phonology in neural models of spoken language

Given the fast development of analysis techniques for NLP and speech processing systems, few systematic studies have been conducted to compare the strengths and weaknesses of each method. As a step in this direction we study the case of representations of phonology in neural network models of spoken language. We use two commonly applied analytical techniques, diagnostic classifiers and representational similarity analysis, to quantify to what extent neural activation patterns encode phonemes and phoneme sequences. We manipulate two factors that can affect the outcome of analysis. First, we investigate the role of learning by comparing neural activations extracted from trained versus randomly-initialized models. Second, we examine the temporal scope of the activations by probing both local activations corresponding to a few milliseconds of the speech signal, and global activations pooled over the whole utterance. We conclude that reporting analysis results with randomly initialized models is crucial, and that global-scope methods tend to yield more consistent results and we recommend their use as a complement to local-scope diagnostic methods.Comment: ACL 202

Improved Neural Relation Detection for Knowledge Base Question Answering

Relation detection is a core component for many NLP applications including Knowledge Base Question Answering (KBQA). In this paper, we propose a hierarchical recurrent neural network enhanced by residual learning that detects KB relations given an input question. Our method uses deep residual bidirectional LSTMs to compare questions and relation names via different hierarchies of abstraction. Additionally, we propose a simple KBQA system that integrates entity linking and our proposed relation detector to enable one enhance another. Experimental results evidence that our approach achieves not only outstanding relation detection performance, but more importantly, it helps our KBQA system to achieve state-of-the-art accuracy for both single-relation (SimpleQuestions) and multi-relation (WebQSP) QA benchmarks.Comment: Accepted by ACL 2017 (updated for camera-ready