Louisa Pragst


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Comparative Study of Sentence Embeddings for Contextual Paraphrasing
Louisa Pragst | Wolfgang Minker | Stefan Ultes
Proceedings of the Twelfth Language Resources and Evaluation Conference

Paraphrasing is an important aspect of natural-language generation that can produce more variety in the way specific content is presented. Traditionally, paraphrasing has been focused on finding different words that convey the same meaning. However, in human-human interaction, we regularly express our intention with phrases that are vastly different regarding both word content and syntactic structure. Instead of exchanging only individual words, the complete surface realisation of a sentences is altered while still preserving its meaning and function in a conversation. This kind of contextual paraphrasing did not yet receive a lot of attention from the scientific community despite its potential for the creation of more varied dialogues. In this work, we evaluate several existing approaches to sentence encoding with regard to their ability to capture such context-dependent paraphrasing. To this end, we define a paraphrase classification task that incorporates contextual paraphrases, perform dialogue act clustering, and determine the performance of the sentence embeddings in a sentence swapping task.


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On the Vector Representation of Utterances in Dialogue Context
Louisa Pragst | Niklas Rach | Wolfgang Minker | Stefan Ultes
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)

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Changing the Level of Directness in Dialogue using Dialogue Vector Models and Recurrent Neural Networks
Louisa Pragst | Stefan Ultes
Proceedings of the 19th Annual SIGdial Meeting on Discourse and Dialogue

In cooperative dialogues, identifying the intent of ones conversation partner and acting accordingly is of great importance. While this endeavour is facilitated by phrasing intentions as directly as possible, we can observe in human-human communication that a number of factors such as cultural norms and politeness may result in expressing one’s intent indirectly. Therefore, in human-computer communication we have to anticipate the possibility of users being indirect and be prepared to interpret their actual meaning. Furthermore, a dialogue system should be able to conform to human expectations by adjusting the degree of directness it uses to improve the user experience. To reach those goals, we propose an approach to differentiate between direct and indirect utterances and find utterances of the opposite characteristic that express the same intent. In this endeavour, we employ dialogue vector models and recurrent neural networks.


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Acquisition and Assessment of Semantic Content for the Generation of Elaborateness and Indirectness in Spoken Dialogue Systems
Louisa Pragst | Koichiro Yoshino | Wolfgang Minker | Satoshi Nakamura | Stefan Ultes
Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

In a dialogue system, the dialogue manager selects one of several system actions and thereby determines the system’s behaviour. Defining all possible system actions in a dialogue system by hand is a tedious work. While efforts have been made to automatically generate such system actions, those approaches are mostly focused on providing functional system behaviour. Adapting the system behaviour to the user becomes a difficult task due to the limited amount of system actions available. We aim to increase the adaptability of a dialogue system by automatically generating variants of system actions. In this work, we introduce an approach to automatically generate action variants for elaborateness and indirectness. Our proposed algorithm extracts RDF triplets from a knowledge base and rates their relevance to the original system action to find suitable content. We show that the results of our algorithm are mostly perceived similarly to human generated elaborateness and indirectness and can be used to adapt a conversation to the current user and situation. We also discuss where the results of our algorithm are still lacking and how this could be improved: Taking into account the conversation topic as well as the culture of the user is likely to have beneficial effect on the user’s perception.


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Cultural Communication Idiosyncrasies in Human-Computer Interaction
Juliana Miehle | Koichiro Yoshino | Louisa Pragst | Stefan Ultes | Satoshi Nakamura | Wolfgang Minker
Proceedings of the 17th Annual Meeting of the Special Interest Group on Discourse and Dialogue

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Automatic Modification of Communication Style in Dialogue Management
Louisa Pragst | Juliana Miehle | Stefan Ultes | Wolfgang Minker
Proceedings of the INLG 2016 Workshop on Computational Creativity in Natural Language Generation