1,919 research outputs found
Dialogue as Data in Learning Analytics for Productive Educational Dialogue
This paper provides a novel, conceptually driven stance on the state of the contemporary analytic challenges faced in the treatment of dialogue as a form of data across on- and offline sites of learning. In prior research, preliminary steps have been taken to detect occurrences of such dialogue using automated analysis techniques. Such advances have the potential to foster effective dialogue using learning analytic techniques that scaffold, give feedback on, and provide pedagogic contexts promoting such dialogue. However, the translation of much prior learning science research to online contexts is complex, requiring the operationalization of constructs theorized in different contexts (often face-to-face), and based on different datasets and structures (often spoken dialogue). In this paper, we explore what could constitute the effective analysis of productive online dialogues, arguing that it requires consideration of three key facets of the dialogue: features indicative of productive dialogue; the unit of segmentation; and the interplay of features and segmentation with the temporal underpinning of learning contexts. The paper thus foregrounds key considerations regarding the analysis of dialogue data in emerging learning analytics environments, both for learning-science and for computationally oriented researchers
Analyzing collaborative learning processes automatically
In this article we describe the emerging area of text classification research focused on the problem of collaborative learning process analysis both from a broad perspective and more specifically in terms of a publicly available tool set called TagHelper tools. Analyzing the variety of pedagogically valuable facets of learners’ interactions is a time consuming and effortful process. Improving automated analyses of such highly valued processes of collaborative learning by adapting and applying recent text classification technologies would make it a less arduous task to obtain insights from corpus data. This endeavor also holds the potential for enabling substantially improved on-line instruction both by providing teachers and facilitators with reports about the groups they are moderating and by triggering context sensitive collaborative learning support on an as-needed basis. In this article, we report on an interdisciplinary research project, which has been investigating the effectiveness of applying text classification technology to a large CSCL corpus that has been analyzed by human coders using a theory-based multidimensional coding scheme. We report promising results and include an in-depth discussion of important issues such as reliability, validity, and efficiency that should be considered when deciding on the appropriateness of adopting a new technology such as TagHelper tools. One major technical contribution of this work is a demonstration that an important piece of the work towards making text classification technology effective for this purpose is designing and building linguistic pattern detectors, otherwise known as features, that can be extracted reliably from texts and that have high predictive power for the categories of discourse actions that the CSCL community is interested in
End-to-end multi-level dialog act recognition
The three-level dialog act annotation scheme of the DIHANA corpus poses a multi-level classification problem in which the bottom levels allow multiple or no labels for a single segment. We approach automatic dialog act recognition on the three levels using an end-to-end approach, in order to implicitly capture relations between them. Our deep neural network classifier uses a combination of word- and character-based segment representation approaches, together with a summary of the dialog history and information concerning speaker changes. We show that it is important to specialize the generic segment representation in order to capture the most relevant information for each level. On the other hand, the summary of the dialog history should combine information from the three levels to capture dependencies between them. Furthermore, the labels generated for each level help in the prediction of those of the lower levels. Overall, we achieve results which surpass those of our previous approach using the hierarchical combination of three independent per-level classifiers. Furthermore, the results even surpass the results achieved on the simplified version of the problem approached by previous studies, which neglected the multi-label nature of the bottom levels and only considered the label combinations present in the corpus.info:eu-repo/semantics/publishedVersio
Accessing spoken interaction through dialogue processing [online]
Zusammenfassung
Unser Leben, unsere Leistungen und unsere Umgebung, alles wird
derzeit durch Schriftsprache dokumentiert. Die rasante
Fortentwicklung der technischen Möglichkeiten Audio, Bilder und
Video aufzunehmen, abzuspeichern und wiederzugeben kann genutzt
werden um die schriftliche Dokumentation von menschlicher
Kommunikation, zum Beispiel Meetings, zu unterstĂĽtzen, zu
ergänzen oder gar zu ersetzen. Diese neuen Technologien können
uns in die Lage versetzen Information aufzunehmen, die
anderweitig verloren gehen, die Kosten der Dokumentation zu
senken und hochwertige Dokumente mit audiovisuellem Material
anzureichern. Die Indizierung solcher Aufnahmen stellt die
Kerntechnologie dar um dieses Potential auszuschöpfen. Diese
Arbeit stellt effektive Alternativen zu schlĂĽsselwortbasierten
Indizes vor, die Suchraumeinschränkungen bewirken und teilweise
mit einfachen Mitteln zu berechnen sind.
Die Indizierung von Sprachdokumenten kann auf verschiedenen
Ebenen erfolgen: Ein Dokument gehört stilistisch einer
bestimmten Datenbasis an, welche durch sehr einfache Merkmale
bei hoher Genauigkeit automatisch bestimmt werden kann.
Durch diese Art von Klassifikation kann eine Reduktion des
Suchraumes um einen Faktor der Größenordnung 4Â10 erfolgen. Die
Anwendung von thematischen Merkmalen zur Textklassifikation
bei einer Nachrichtendatenbank resultiert in einer Reduktion um
einen Faktor 18. Da Sprachdokumente sehr lang sein können müssen
sie in thematische Segmente unterteilt werden. Ein neuer
probabilistischer Ansatz sowie neue Merkmale (SprecherinitiaÂ
tive und Stil) liefern vergleichbare oder bessere Resultate als
traditionelle schlüsselwortbasierte Ansätze. Diese thematische
Segmente können durch die vorherrschende Aktivität
charakterisiert werden (erzählen, diskutieren, planen, ...),
die durch ein neuronales Netz detektiert werden kann. Die
Detektionsraten sind allerdings begrenzt da auch Menschen
diese Aktivitäten nur ungenau bestimmen. Eine maximale
Reduktion des Suchraumes um den Faktor 6 ist bei den verwendeten
Daten theoretisch möglich. Eine thematische Klassifikation
dieser Segmente wurde ebenfalls auf einer Datenbasis
durchgefĂĽhrt, die Detektionsraten fĂĽr diesen Index sind jedoch
gering.
Auf der Ebene der einzelnen Äußerungen können Dialogakte wie
Aussagen, Fragen, RĂĽckmeldungen (aha, ach ja, echt?, ...) usw.
mit einem diskriminativ trainierten Hidden Markov Model erkannt
werden. Dieses Verfahren kann um die Erkennung von kurzen Folgen
wie Frage/AntwortÂSpielen erweitert werden (Dialogspiele).
Dialogakte und Âspiele können eingesetzt werden um
Klassifikatoren fĂĽr globale Sprechstile zu bauen. Ebenso
könnte ein Benutzer sich an eine bestimmte Dialogaktsequenz
erinnern und versuchen, diese in einer grafischen
Repräsentation wiederzufinden.
In einer Studie mit sehr pessimistischen Annahmen konnten
Benutzer eines aus vier ähnlichen und gleichwahrscheinlichen
Gesprächen mit einer Genauigkeit von ~ 43% durch eine graphische
Repräsentation von Aktivität bestimmt.
Dialogakte könnte in diesem Szenario ebenso nützlich sein, die
Benutzerstudie konnte aufgrund der geringen Datenmenge darĂĽber
keinen endgĂĽltigen AufschluĂź geben. Die Studie konnte allerdings
für detailierte Basismerkmale wie Formalität und
Sprecheridentität keinen Effekt zeigen.
Abstract
Written language is one of our primary means for documenting our
lives, achievements, and environment. Our capabilities to
record, store and retrieve audio, still pictures, and video are
undergoing a revolution and may support, supplement or even
replace written documentation. This technology enables us to
record information that would otherwise be lost, lower the cost
of documentation and enhance highÂquality documents with
original audiovisual material.
The indexing of the audio material is the key technology to
realize those benefits. This work presents effective
alternatives to keyword based indices which restrict the search
space and may in part be calculated with very limited resources.
Indexing speech documents can be done at a various levels:
Stylistically a document belongs to a certain database which can
be determined automatically with high accuracy using very simple
features. The resulting factor in search space reduction is in
the order of 4Â10 while topic classification yielded a factor
of 18 in a news domain.
Since documents can be very long they need to be segmented into
topical regions. A new probabilistic segmentation framework as
well as new features (speaker initiative and style) prove to be
very effective compared to traditional keyword based methods. At
the topical segment level activities (storytelling, discussing,
planning, ...) can be detected using a machine learning approach
with limited accuracy; however even human annotators do not
annotate them very reliably. A maximum search space reduction
factor of 6 is theoretically possible on the databases used. A
topical classification of these regions has been attempted
on one database, the detection accuracy for that index, however,
was very low.
At the utterance level dialogue acts such as statements,
questions, backchannels (aha, yeah, ...), etc. are being
recognized using a novel discriminatively trained HMM procedure.
The procedure can be extended to recognize short sequences such
as question/answer pairs, so called dialogue games.
Dialog acts and games are useful for building classifiers for
speaking style. Similarily a user may remember a certain dialog
act sequence and may search for it in a graphical
representation.
In a study with very pessimistic assumptions users are able to
pick one out of four similar and equiprobable meetings correctly
with an accuracy ~ 43% using graphical activity information.
Dialogue acts may be useful in this situation as well but the
sample size did not allow to draw final conclusions. However the
user study fails to show any effect for detailed basic features
such as formality or speaker identity
Reconocimiento de acto de diálogo secuencial para debates argumentativos árabes
Dialogue act recognition remains a primordial task that helps user to automatically identify participants’ intentions. In this paper, we propose a sequential approach consisting of segmentation followed by annotation process to identify dialogue acts within Arabic politic debates. To perform DA recognition, we used the CARD corpus labeled using the SADA annotation schema. Segmentation and annotation tasks were then carried out using Conditional Random Fields probabilistic models as they prove high performance in segmenting and labeling sequential data. Learning results are notably important for the segmentation task (F-score=97.9%) and relatively reliable within the annotation process (f-score=63.4%) given the complexity of identifying argumentative tags and the presence of disfluencies in spoken conversations.El reconocimiento del acto de diálogo sigue siendo una tarea primordial que ayuda al usuario a identificar automáticamente las intenciones de los participantes. En este documento, proponemos un enfoque secuencial que consiste en la segmentaciĂłn seguida de un proceso de anotaciĂłn para identificar actos de diálogo dentro de los debates polĂticos árabes. Para realizar el reconocimiento DA, utilizamos el corpus CARD etiquetado utilizando el esquema de anotaciĂłn SADA. Las tareas de segmentaciĂłn y anotaciĂłn se llevaron a cabo utilizando modelos probabilĂsticos de Campos aleatorios condicionales, ya que demuestran un alto rendimiento en la segmentaciĂłn y el etiquetado de datos secuenciales. Los resultados de aprendizaje son especialmente importantes para la tarea de segmentaciĂłn (F-score = 97.9%) y relativamente confiables dentro del proceso de anotaciĂłn (f-score = 63.4%) dada la complejidad de identificar etiquetas argumentativas y la presencia de disfluencias en las conversaciones habladas
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