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Effects of Spatial Speech Presentation on Listener Response Strategy for Talker-Identification ...
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Modeling contextual information in neural machine translation
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Distributed representations for multilingual language processing
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Towards a Corpus of Historical German Plays with Emotion Annotations ...
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Exploring Online Depression Forums via Text Mining: A Comparison of Reddit and a Curated Online Forum ...
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Towards the Analysis of Fan Fictions in German Language: Exploration of a Corpus from the Platform Archive of Our Own ...
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Emotion Classification in German Plays with Transformer-based Language Models Pretrained on Historical and Contemporary Language ...
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Korpuslinguistik in der Rechtswissenschaft. Eine webbasierte Analyseplattform für EuGH-Entscheidungen ...
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Exploring Multimodal Sentiment Analysis in Plays: A Case Study for a Theater Recording of Emilia Galotti ...
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Lexicon-based Sentiment Analysis in German: Systematic Evaluation of Resources and Preprocessing Techniques ...
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Pragmatic Prediction in the Processing of Referring Expressions Containing Scalar Quantifiers ...
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Neural Network Learning for Robust Speech Recognition
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Qu, Leyuan. - : Staats- und Universitätsbibliothek Hamburg Carl von Ossietzky, 2021
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Student Performance and Collaboration in Introductory Courses to Theory of Computation ; Studierendenperformance und Kollaboration in Einführungskursen der Theoretischen Informatik
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Frede, Christiane. - : Staats- und Universitätsbibliothek Hamburg Carl von Ossietzky, 2021
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Classifying user information needs in cooking dialogues – an empirical performance evaluation of transformer networks
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Abstract:
In this master’s thesis, I carry out 3720 machine learning experiments. I want to test how transformer networks perform in a dialogue processing task. Transformer networks are deep neural networks that have first been proposed in 2017 and have since rapidly set new state of the art results on many tasks. To evaluate their performance in dialogue classification, I use two tasks from two datasets. One comes from a dialogue; the other does not. I compare various transformer network’s F1 scores on these two classification tasks. I also look at many different baseline models, from random forest classifiers to long short-term memory networks. A theoretically derived taxonomy will be used to annotate dialogue data with information on dialogue flow. I will show that modelling human conversation is an intricate task and that more features do not necessarily make classification better. Five hypotheses are tested using statistical methods on the output data from the 3720 experiments. Those analyses show that results are very alike for the same machine learning algorithms on the two different tasks. Beyond performance evaluation, the aim is to use transformers to improve user information need classification. These needs I am examining in this study arise during assisted cooking dialogues with a conversational agent. Generally, I can show that transformer networks achieve better classification results than the established baseline models.
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Keyword:
004 Informatik; 020 Bibliotheks- und Informationswissenschaft; 320 Politik; 400 Sprachwissenschaft; ddc:004; ddc:020; ddc:320; ddc:400; Linguistik
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URL: https://epub.uni-regensburg.de/46308/2/Masterarbeit%20Patrick%20Schwabl.zip https://epub.uni-regensburg.de/46308/1/Masterarbeit,%20Patrick%20Schwabl.pdf https://epub.uni-regensburg.de/46308/
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