Emotion and Adaptation

A wide variety of media can be used in learning, including distance learning, such as print, lectures, conference sections, tutors, pictures, video, sound, and computers. Any one instance of distance learning will make choices among these media, perhaps using several.

Multimedia in Learning

Politeness: some universals in language usage

The Cambridge handbook of multimedia learning

This paper surveys the current state of the art in Natural Language Generation (NLG), defined as the task of generating text or speech from non-linguistic input. A survey of NLG is timely in view of the changes that the field has undergone over the past two decades, especially in relation to new (usually data-driven) methods, as well as new applications of NLG technology. This survey therefore aims to (a) give an up-to-date synthesis of research on the core tasks in NLG and the architectures adopted in which such tasks are organised; (b) highlight a number of recent research topics that have arisen partly as a result of growing synergies between NLG and other areas of artifical intelligence; (c) draw attention to the challenges in NLG evaluation, relating them to similar challenges faced in other areas of nlp, with an emphasis on different evaluation methods and the relationships between them.

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Survey of the state of the art in natural language generation: core tasks, applications and evaluation

We present KAF, the KYOTO Annotation Format. KAF is a layered and extendible linguistic annotation format that is specifically developed to arrive at semantic interoperability. KAF is used in seven languages in several applications throughout the KYOTO (Knowledge Yielding Ontologies for Transition-based Organization) project. The goal of these applications is to derive semantic data from linguistically processed text. Separate annotation layers are defined for each annotation process but these can be combined to arrive at a higher level of semantic representation. This paper gives an outline of KAF and a description of how it is applied in the KYOTO project.

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KAF: a Generic Semantic Annotation Format

Analysis of student-tutor coaching dialogs suggest that good human tutors attend to and attempt to influence the motivational state of learners. Moreover, they are sensitive to the social face of the learner, and seek to mitigate the potential face threat of their comments. This paper describes a dialog generator for pedagogical agents that takes motivation and face threat factors into account. This enables the agent to interact with learners in a socially appropriate fashion, and foster intrinsic motivation on the part of the learner, which in turn may lead to more positive learner affective states.

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Generating socially appropriate tutorial dialog

We describe a method for recognizing textual entailment that uses the length of the longest common subsequence (LCS) between two texts as its decision criterion. Rather than requiring strict word matching in the common subsequences, we perform a flexible match using automatically generated paraphrases.We find that the use of paraphrases over strict word matches represents an average F-measure improvement from 0.22 to 0.36 on the CLEF 2006 Answer Validation Exercise for 7 languages.

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Paraphrase substitution for recognizing textual entailment

This paper addresses syntax-based paraphrasing methods for Recognizing Textual Entailment (RTE). In particular, we describe a dependency-based paraphrasing algorithm, using the DIRT data set, and its application in the context of a straightforward RTE system based on aligning dependency trees. We find a small positive effect of dependency-based paraphrasing on both the RTE3 development and test sets, but the added value of this type of paraphrasing deserves further analysis.

/pdf/dependency-based-paraphrasing-for-recognizing-textual-a5vtrtsmn7.pdf

Dependency-based paraphrasing for recognizing textual entailment

Research on Question Answering is focused mainly on classifying the question type and finding the answer. Presenting the answer in a way that suits the user’s needs has received little attention. This paper shows how existing question answering systems—which aim at finding precise answers to questions—can be improved by exploiting summarization techniques to extract more than just the answer from the document in which the answer resides. This is done using a graph search algorithm which searches for relevant sentences in the discourse structure, which is represented as a graph. The Rhetorical Structure Theory (RST) is used to create a graph representation of a text document. The output is an extensive answer, which not only answers the question, but also gives the user an opportunity to assess the accuracy of the answer (is this what I am looking for?), and to find additional information that is related to the question, and which may satisfy an information need. This has been implemented in a working multimodal question answering system where it operates with two independently developed question answering modules.

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W.E. Bosma

Papers

KAF: a Generic Semantic Annotation Format

Generating socially appropriate tutorial dialog

Paraphrase substitution for recognizing textual entailment

Dependency-based paraphrasing for recognizing textual entailment

Query-Based Summarization using Rhetorical Structure Theory