References of "Höhn, Sviatlana 50002002"
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See detailA Personalized Sentiment Model with Textual and Contextual Information
Guo, Siwen UL; Höhn, Sviatlana UL; Schommer, Christoph UL

in The SIGNLL Conference on Computational Natural Language Learning, Hong Kong 3-4 November 2019 (2019, November)

In this paper, we look beyond the traditional population-level sentiment modeling and consider the individuality in a person's expressions by discovering both textual and contextual information. In ... [more ▼]

In this paper, we look beyond the traditional population-level sentiment modeling and consider the individuality in a person's expressions by discovering both textual and contextual information. In particular, we construct a hierarchical neural network that leverages valuable information from a person's past expressions, and offer a better understanding of the sentiment from the expresser's perspective. Additionally, we investigate how a person's sentiment changes over time so that recent incidents or opinions may have more effect on the person's current sentiment than the old ones. Psychological studies have also shown that individual variation exists in how easily people change their sentiments. In order to model such traits, we develop a modified attention mechanism with Hawkes process applied on top of a recurrent network for a user-specific design. Implemented with automatically labeled Twitter data, the proposed model has shown positive results employing different input formulations for representing the concerned information. [less ▲]

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See detailArtificial Companion for Second Language Conversation
Höhn, Sviatlana UL

Book published by Springer International Publishing (2019)

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See detailTopic-based Historical Information Selection for Personalized Sentiment Analysis
Guo, Siwen UL; Höhn, Sviatlana UL; Schommer, Christoph UL

in European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, Bruges 24-26 April 2019 (2019, April)

In this paper, we present a selection approach designed for personalized sentiment analysis with the aim of extracting related information from a user's history. Analyzing a person's past is key to ... [more ▼]

In this paper, we present a selection approach designed for personalized sentiment analysis with the aim of extracting related information from a user's history. Analyzing a person's past is key to modeling individuality and understanding the current state of the person. We consider a user's expressions in the past as historical information, and target posts from social platforms for which Twitter texts are chosen as exemplary. While implementing the personalized model PERSEUS, we observed information loss due to the lack of flexibility regarding the design of the input sequence. To compensate this issue, we provide a procedure for information selection based on the similarities in the topics of a user's historical posts. Evaluation is conducted comparing different similarity measures, and improvements are seen with the proposed method. [less ▲]

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See detailLooking into the Past: Evaluating the Effect of Time Gaps in a Personalized Sentiment Model
Guo, Siwen UL; Höhn, Sviatlana UL; Schommer, Christoph UL

in ACM/SIGAPP Symposium On Applied Computing, Limassol 8-12 April 2019 (2019, April)

This paper concerns personalized sentiment analysis, which aims at improving the prediction of the sentiment expressed in a piece of text by considering individualities. Mostly, this is done by relating ... [more ▼]

This paper concerns personalized sentiment analysis, which aims at improving the prediction of the sentiment expressed in a piece of text by considering individualities. Mostly, this is done by relating to a person’s past expressions (or opinions), however the time gaps between the messages are not considered in the existing works. We argue that the opinion at a specific time point is affected more by recent opinions that contain related content than the earlier or unrelated ones, thus a sentiment model ought to include such information in the analysis. By using a recurrent neural network with an attention layer as a basic model, we introduce three cases to integrate time gaps in the model. Evaluated on Twitter data with frequent users, we have found that the performance is improved the most by including the time information in the Hawkes process, and it is also more effective to add the time information in the attention layer than at the input. [less ▲]

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See detailPersonalized Sentiment Analysis and a Framework with Attention-Based Hawkes Process Model
Guo, Siwen UL; Höhn, Sviatlana UL; Xu, Feiyu et al

in Agents and Artificial Intelligence (2019)

People use different words when expressing their opinions. Sentiment analysis as a way to automatically detect and categorize people’s opinions in text, needs to reflect this diversity and individuality ... [more ▼]

People use different words when expressing their opinions. Sentiment analysis as a way to automatically detect and categorize people’s opinions in text, needs to reflect this diversity and individuality. One possible approach to analyze such traits is to take a person’s past opinions into consideration. In practice, such a model can suffer from the data sparsity issue, thus it is difficult to develop. In this article, we take texts from social platforms and propose a preliminary model for evaluating the effectiveness of including user information from the past, and offer a solution for the data sparsity. Furthermore, we present a finer-designed, enhanced model that focuses on frequent users and offers to capture the decay of past opinions using various gaps between the creation time of the text. An attention-based Hawkes process on top of a recurrent neural network is applied for this purpose, and the performance of the model is evaluated with Twitter data. With the proposed framework, positive results are shown which opens up new perspectives for future research. [less ▲]

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See detailConversational Interfaces for Explainable AI: A Human-Centered Approach
Jenzsch, Sophie; Höhn, Sviatlana UL; Hochgeschwender, Nico UL

in Proceedings of EXTRAAMAS 2019 (2019)

One major goal of Explainable Artificial Intelligence (XAI), in order to enhance trust in technology, is to enable the user to enquire information and explanation about its functionality directly from an ... [more ▼]

One major goal of Explainable Artificial Intelligence (XAI), in order to enhance trust in technology, is to enable the user to enquire information and explanation about its functionality directly from an intelligent agent. We propose conversational interfaces (CI) to be the perfect setting, since they are intuitive for humans and computationally processible. While there are many approaches addressing technical issues of this human-agent communication problem, the user perspective appears to be widely neglected. With the purpose of better requirement understanding and identification of implicit expectations from a human-centered view, a Wizard of Oz experiment was conducted, where participants tried to elicit basic information from a pretended artificial agent (What are your capabilities?). The hypothesis that users pursue fundamentally different strategies could be verified with the help of Conversation Analysis. Results illustrate the vast variety in human communication and disclose both requirements of users and obstacles in the implementation of protocols for interacting agents. Finally, we infer essential indications for the implementation of such a CI. [less ▲]

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See detailQuizbot: Exploring Formative Feedback with Conversational Interfaces
Vijayakumar, Bharathi UL; Höhn, Sviatlana UL; Schommer, Christoph UL

in Vijayakumar, Bharathi; Höhn, Sviatlana; Schommer, Christoph (Eds.) Proceedings of the (2018)

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See detailA data-driven model of explanations for a chatbot that helps to practice conversation in a foreign language
Höhn, Sviatlana UL

in Proceedings of SIGDial 2017 (2017)

This article describes a model of other-initiated self-repair for a chatbot that helps to practice conversation in a foreign lan- guage. The model was developed using a corpus of instant messaging ... [more ▼]

This article describes a model of other-initiated self-repair for a chatbot that helps to practice conversation in a foreign lan- guage. The model was developed using a corpus of instant messaging conversations between German native and non-native speakers. Conversation Analysis helped to create computational models from a small number of examples. The model has been validated in an AIML-based chatbot. Unlike typical retrieval-based dialogue systems, the explanations are generated at run-time from a linguistic database. [less ▲]

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See detailData-driven Repair Models for Text Chat with Language Learners
Höhn, Sviatlana UL

Doctoral thesis (2016)

This research analyses participants' orientation to linguistic identities in chat and introduces data-driven computational models for communicative Intelligent Computer-Assisted Language Learning ... [more ▼]

This research analyses participants' orientation to linguistic identities in chat and introduces data-driven computational models for communicative Intelligent Computer-Assisted Language Learning (communicative ICALL). Based on non-pedagogical chat conversations between native speakers and non-native speakers, computational models of the following types are presented: exposed and embedded corrections, explanations of unknown words following learner's request. Conversation Analysis helped to obtain patterns from a corpus of dyadic chat conversations in a longitudinal setting, bringing together German native speakers and advanced learners of German as a foreign language. More specifically, this work states a bottom-up, data-driven research design which takes “conversation” from its genuine personalised dyadic environment to a model of a conversational agent. It allows for an informal functional specification of such an agent to which a technical specification for two specific repair types is provided. Starting with the open research objective to create a machine that behaves like a language expert in an informal conversation, this research shows that various forms of orientation to linguistic identities are on participants' disposal in chat. In addition it shows that dealing with computational complexity can be approached by a separation between local models of specific practices and a high-level regulatory mechanism to activate them. More specifically, this work shows that learners' repair initiations may be analysed as turn formats containing resources for signalling trouble and referencing trouble source. Based on this finding, this work shows how computational models for recognition of the repair initiations and trouble source extraction can be formalised and implemented in a chatbot. Further, this work makes clear which level of description of error corrections is required to satisfy computational needs, and how these descriptions may be transformed to patterns for various error correction formats and which technological requirements they imply. Finally, this research shows which factors in interaction influence the decision to correct and how the creation of a high-level decision model for error correction in a Conversation-for-Learning can be approached. In sum, this research enriches the landscape of various communication setups between language learners and communicative ICALL systems explicitly covering Conversations-for-Learning. It strengthens multidisciplinary connections by showing how the multidisciplinary research field of ICALL benefits from including Conversation Analysis into the research paradigm. It highlights the impact of the micro-analytic understanding of actions accomplished by utterances in talk within a specific speech exchange system on computational modelling on the example of chat with language learners. [less ▲]

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See detailInteraction Profiles for an Artificial Conversational Companion
Höhn, Sviatlana UL; Busemann, Stephan; Max, Charles UL et al

Scientific Conference (2015, September)

Using Artificial Companions for tasks requiring long-term interaction like language learning or coaching can be approached by creating local computational models for particular interaction structures, and ... [more ▼]

Using Artificial Companions for tasks requiring long-term interaction like language learning or coaching can be approached by creating local computational models for particular interaction structures, and models reflecting changes in interaction over time. An Artificial Conversational Companion (ACC) that helps to practice conversation in a foreign language is expected to play the role of a language expert in conversation. We apply methods of Conversation Analysis to obtain data- driven models of interaction profiles for language experts and language novices from a corpus of instant messaging based dialogues between native speakers of German and advanced learners of German as a foreign language. We show different ways how the artificial agent can simulate ”doing being expert” in conversation and promote learning. [less ▲]

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See detailCorpus of long-term instant messaging based dialogues between advanced learners of German as a foreign language and German native speakers: deL1L2IM
Höhn, Sviatlana UL

Computer development (2015)

The deL1L2IM corpus, created between May and August 2012 and last updated in August 2014, has been collected within the framework of a PhD project on the development of a learning method implying ... [more ▼]

The deL1L2IM corpus, created between May and August 2012 and last updated in August 2014, has been collected within the framework of a PhD project on the development of a learning method implying conversations with an artificial companion. This PhD work is presented as a qualitative investigation of instant messaging dialogues on a long-term basis (four months) between advanced learners of German and German native speakers, chatting about whatever topic they wish. The dataset is composed of 72 dialogues, each of them having a duration of 20 to 45 minutes. The whole corpus contains ca. 52,000 words and 4,800 messages and has a file size of 0.5 Mb. Nine pairs of participants – i.e. nine learners and four native speakers – were required, with 8 dialogues per pair. The interactions have undergone linguistic analysis whereby the annotation will be performed only on repair/correction sequences (incomplete learner error annotation). The goal of the project was to create an application for language modelling and to improve learner language applications, tutoring software and dialogue systems. The corpus is delivered in one written text file (in XML format, customized under TEI P5). [less ▲]

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See detailDealing with Trouble: A Data-Driven Model of a Repair Type for a Conversational Agent
Höhn, Sviatlana UL

in Proceedings of the twenty-ninth AAAI conference on Artificial Intelligence (2015)

Detailed reference viewed: 178 (15 UL)