12,472 research outputs found
Query-Based Summarization using Rhetorical Structure Theory
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 userâs needs has received little
attention. This paper shows how existing question answering systemsâwhich aim at finding
precise answers to questionsâcan 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
Building Knowledge Bases for the Generation of Software Documentation
Automated text generation requires a underlying knowledge base from which to
generate, which is often difficult to produce. Software documentation is one
domain in which parts of this knowledge base may be derived automatically. In
this paper, we describe \drafter, an authoring support tool for generating
user-centred software documentation, and in particular, we describe how parts
of its required knowledge base can be obtained automatically.Comment: 6 pages, from COLING-9
Analytic frameworks for assessing dialogic argumentation in online learning environments
Over the last decade, researchers have developed sophisticated online learning environments to support students engaging in argumentation. This review first considers the range of functionalities incorporated within these online environments. The review then presents five categories of analytic frameworks focusing on (1) formal argumentation structure, (2) normative quality, (3) nature and function of contributions within the dialog, (4) epistemic nature of reasoning, and (5) patterns and trajectories of participant interaction. Example analytic frameworks from each category are presented in detail rich enough to illustrate their nature and structure. This rich detail is intended to facilitate researchersâ identification of possible frameworks to draw upon in developing or adopting analytic methods for their own work. Each framework is applied to a shared segment of student dialog to facilitate this illustration and comparison process. Synthetic discussions of each category consider the frameworks in light of the underlying theoretical perspectives on argumentation, pedagogical goals, and online environmental structures. Ultimately the review underscores the diversity of perspectives represented in this research, the importance of clearly specifying theoretical and environmental commitments throughout the process of developing or adopting an analytic framework, and the role of analytic frameworks in the future development of online learning environments for argumentation
Goals in Discourse - From Actions to Rhetorical Relations
This book provides a better understanding of rhetorical relations and their impact on the referent's abilities to
serve as non-local antecedents for referential expressions.
It argues that rhetorical relations rather relate speech acts than semantic denotations of sentences, which
emphasizes the action theoretic character of communication and which makes it possible to describe and understand
rhetorical relations for non-assertive speech acts.
The arguments in this book are based on some cognitive and action theoretical assumptions
which help to model a hearer's recognition of the speaker's communicative plans, which is believed to
be the unrealized discourse structure.
In particular, the common sense principle of inertia is used, which models the general assumption that
agents believe that things in the world do not change if there is no reason for them to change.
Based on this principle, it is possible to explain why acceptance moves in dialog are necessary for communication and,
moreover, why by default, two speech acts are related by a support relation, i.e. by a relation where
one speech act supports the goal of the speech act it is related to.
Support relations are described as a further class of rhetorical relations that, roughly, correspond to subordinating relations
but, in contrast to subordinating relations, have the advantage that it is possible to provide an intrinsic definition.
Finally, it is shown how discourse structure as an impact on the prominence status of referents and speech acts, being roughly the ability
of referents to serve as structural anchor for further communication
Deep Dialog Act Recognition using Multiple Token, Segment, and Context Information Representations
Dialog act (DA) recognition is a task that has been widely explored over the
years. Recently, most approaches to the task explored different DNN
architectures to combine the representations of the words in a segment and
generate a segment representation that provides cues for intention. In this
study, we explore means to generate more informative segment representations,
not only by exploring different network architectures, but also by considering
different token representations, not only at the word level, but also at the
character and functional levels. At the word level, in addition to the commonly
used uncontextualized embeddings, we explore the use of contextualized
representations, which provide information concerning word sense and segment
structure. Character-level tokenization is important to capture
intention-related morphological aspects that cannot be captured at the word
level. Finally, the functional level provides an abstraction from words, which
shifts the focus to the structure of the segment. We also explore approaches to
enrich the segment representation with context information from the history of
the dialog, both in terms of the classifications of the surrounding segments
and the turn-taking history. This kind of information has already been proved
important for the disambiguation of DAs in previous studies. Nevertheless, we
are able to capture additional information by considering a summary of the
dialog history and a wider turn-taking context. By combining the best
approaches at each step, we achieve results that surpass the previous
state-of-the-art on generic DA recognition on both SwDA and MRDA, two of the
most widely explored corpora for the task. Furthermore, by considering both
past and future context, simulating annotation scenario, our approach achieves
a performance similar to that of a human annotator on SwDA and surpasses it on
MRDA.Comment: 38 pages, 7 figures, 9 tables, submitted to JAI
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