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Language Planning as a Sociolinguistic Experiment

By: Ernst Jahr

Provides richly detailed insight into the uniqueness of the Norwegian language development. Marks the 200th anniversary of the birth of the Norwegian nation following centuries of Danish rule


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Acquiring Phonology: A Cross-Generational Case-Study

By Neil Smith

The study also highlights the constructs of current linguistic theory, arguing for distinctive features and the notion 'onset' and against some of the claims of Optimality Theory and Usage-based accounts.


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Language Production and Interpretation: Linguistics meets Cognition

By Henk Zeevat

The importance of Henk Zeevat's new monograph cannot be overstated. [...] I recommend it to anyone who combines interests in language, logic, and computation [...]. David Beaver, University of Texas at Austin


Academic Paper


Title: 'Correlations between Dialogue Acts and Learning in Spoken Tutoring Dialogues'
DianeLitman
Institution: 'University of Pittsburgh'
Author: KateForbes-Riley
Institution: 'University of Pittsburgh'
Linguistic Field: 'Applied Linguistics; Computational Linguistics; Text/Corpus Linguistics'
Abstract: We examine correlations between dialogue behaviors and learning in tutoring, using two corpora of spoken tutoring dialogues: a human-human corpus and a human-computer corpus. To formalize the notion of dialogue behavior, we manually annotate our data using a tagset of student and tutor dialogue acts relative to the tutoring domain. A unigram analysis of our annotated data shows that student learning correlates both with the tutor's dialogue acts and with the student's dialogue acts. A bigram analysis shows that student learning also correlates with joint patterns of tutor and student dialogue acts. In particular, our human-computer results show that the presence of student utterances that display reasoning (whether correct or incorrect), as well as the presence of reasoning questions asked by the computer tutor, both positively correlate with learning. Our human-human results show that student introductions of a new concept into the dialogue positively correlates with learning, but student attempts at deeper reasoning (particularly when incorrect), and the human tutor's attempts to direct the dialogue, both negatively correlate with learning. These results suggest that while the use of dialogue act n-grams is a promising method for examining correlations between dialogue behavior and learning, specific findings can differ in human versus computer tutoring, with the latter better motivating adaptive strategies for implementation.

CUP at LINGUIST

This article appears in Natural Language Engineering Vol. 12, Issue 2, which you can read on Cambridge's site or on LINGUIST .



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