Nils Feldhus


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An Annotated Corpus of Textual Explanations for Clinical Decision Support
Roland Roller | Aljoscha Burchardt | Nils Feldhus | Laura Seiffe | Klemens Budde | Simon Ronicke | Bilgin Osmanodja
Proceedings of the Thirteenth Language Resources and Evaluation Conference

In recent years, machine learning for clinical decision support has gained more and more attention. In order to introduce such applications into clinical practice, a good performance might be essential, however, the aspect of trust should not be underestimated. For the treating physician using such a system and being (legally) responsible for the decision made, it is particularly important to understand the system’s recommendation. To provide insights into a model’s decision, various techniques from the field of explainability (XAI) have been proposed whose output is often enough not targeted to the domain experts that want to use the model. To close this gap, in this work, we explore how explanations could possibly look like in future. To this end, this work presents a dataset of textual explanations in context of decision support. Within a reader study, human physicians estimated the likelihood of possible negative patient outcomes in the near future and justified each decision with a few sentences. Using those sentences, we created a novel corpus, annotated with different semantic layers. Moreover, we provide an analysis of how those explanations are constructed, and how they change depending on physician, on the estimated risk and also in comparison to an automatic clinical decision support system with feature importance.

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Towards Personality-Aware Chatbots
Daniel Fernau | Stefan Hillmann | Nils Feldhus | Tim Polzehl | Sebastian Möller
Proceedings of the 23rd Annual Meeting of the Special Interest Group on Discourse and Dialogue

Chatbots are increasingly used to automate operational processes in customer service. However, most chatbots lack adaptation towards their users which may results in an unsatisfactory experience. Since knowing and meeting personal preferences is a key factor for enhancing usability in conversational agents, in this study we analyze an adaptive conversational agent that can automatically adjust according to a user’s personality type carefully excerpted from the Myers-Briggs type indicators. An experiment including 300 crowd workers examined how typifications like extroversion/introversion and thinking/feeling can be assessed and designed for a conversational agent in a job recommender domain. Our results validate the proposed design choices, and experiments on a user-matched personality typification, following the so-called law of attraction rule, show a significant positive influence on a range of selected usability criteria such as overall satisfaction, naturalness, promoter score, trust and appropriateness of the conversation.


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Thermostat: A Large Collection of NLP Model Explanations and Analysis Tools
Nils Feldhus | Robert Schwarzenberg | Sebastian Möller
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing: System Demonstrations

In the language domain, as in other domains, neural explainability takes an ever more important role, with feature attribution methods on the forefront. Many such methods require considerable computational resources and expert knowledge about implementation details and parameter choices. To facilitate research, we present Thermostat which consists of a large collection of model explanations and accompanying analysis tools. Thermostat allows easy access to over 200k explanations for the decisions of prominent state-of-the-art models spanning across different NLP tasks, generated with multiple explainers. The dataset took over 10k GPU hours (> one year) to compile; compute time that the community now saves. The accompanying software tools allow to analyse explanations instance-wise but also accumulatively on corpus level. Users can investigate and compare models, datasets and explainers without the need to orchestrate implementation details. Thermostat is fully open source, democratizes explainability research in the language domain, circumvents redundant computations and increases comparability and replicability.

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European Language Grid: A Joint Platform for the European Language Technology Community
Georg Rehm | Stelios Piperidis | Kalina Bontcheva | Jan Hajic | Victoria Arranz | Andrejs Vasiļjevs | Gerhard Backfried | Jose Manuel Gomez-Perez | Ulrich Germann | Rémi Calizzano | Nils Feldhus | Stefanie Hegele | Florian Kintzel | Katrin Marheinecke | Julian Moreno-Schneider | Dimitris Galanis | Penny Labropoulou | Miltos Deligiannis | Katerina Gkirtzou | Athanasia Kolovou | Dimitris Gkoumas | Leon Voukoutis | Ian Roberts | Jana Hamrlova | Dusan Varis | Lukas Kacena | Khalid Choukri | Valérie Mapelli | Mickaël Rigault | Julija Melnika | Miro Janosik | Katja Prinz | Andres Garcia-Silva | Cristian Berrio | Ondrej Klejch | Steve Renals
Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrations

Europe is a multilingual society, in which dozens of languages are spoken. The only option to enable and to benefit from multilingualism is through Language Technologies (LT), i.e., Natural Language Processing and Speech Technologies. We describe the European Language Grid (ELG), which is targeted to evolve into the primary platform and marketplace for LT in Europe by providing one umbrella platform for the European LT landscape, including research and industry, enabling all stakeholders to upload, share and distribute their services, products and resources. At the end of our EU project, which will establish a legal entity in 2022, the ELG will provide access to approx. 1300 services for all European languages as well as thousands of data sets.

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Efficient Explanations from Empirical Explainers
Robert Schwarzenberg | Nils Feldhus | Sebastian Möller
Proceedings of the Fourth BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP

Amid a discussion about Green AI in which we see explainability neglected, we explore the possibility to efficiently approximate computationally expensive explainers. To this end, we propose feature attribution modelling with Empirical Explainers. Empirical Explainers learn from data to predict the attribution maps of expensive explainers. We train and test Empirical Explainers in the language domain and find that they model their expensive counterparts surprisingly well, at a fraction of the cost. They could thus mitigate the computational burden of neural explanations significantly, in applications that tolerate an approximation error.

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Combining Open Domain Question Answering with a Task-Oriented Dialog System
Jan Nehring | Nils Feldhus | Harleen Kaur | Akhyar Ahmed
Proceedings of the 1st Workshop on Document-grounded Dialogue and Conversational Question Answering (DialDoc 2021)

We apply the modular dialog system framework to combine open-domain question answering with a task-oriented dialog system. This meta dialog system can answer questions from Wikipedia and at the same time act as a personal assistant. The aim of this system is to combine the strength of an open-domain question answering system with the conversational power of task-oriented dialog systems. After explaining the technical details of the system, we combined a new dataset out of standard datasets to evaluate the system. We further introduce an evaluation method for this system. Using this method, we compare the performance of the non-modular system with the performance of the modular system and show that the modular dialog system framework is very suitable for this combination of conversational agents and that the performance of each agent decreases only marginally through the modular setting.


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Towards an Interoperable Ecosystem of AI and LT Platforms: A Roadmap for the Implementation of Different Levels of Interoperability
Georg Rehm | Dimitris Galanis | Penny Labropoulou | Stelios Piperidis | Martin Welß | Ricardo Usbeck | Joachim Köhler | Miltos Deligiannis | Katerina Gkirtzou | Johannes Fischer | Christian Chiarcos | Nils Feldhus | Julian Moreno-Schneider | Florian Kintzel | Elena Montiel | Víctor Rodríguez Doncel | John Philip McCrae | David Laqua | Irina Patricia Theile | Christian Dittmar | Kalina Bontcheva | Ian Roberts | Andrejs Vasiļjevs | Andis Lagzdiņš
Proceedings of the 1st International Workshop on Language Technology Platforms

With regard to the wider area of AI/LT platform interoperability, we concentrate on two core aspects: (1) cross-platform search and discovery of resources and services; (2) composition of cross-platform service workflows. We devise five different levels (of increasing complexity) of platform interoperability that we suggest to implement in a wider federation of AI/LT platforms. We illustrate the approach using the five emerging AI/LT platforms AI4EU, ELG, Lynx, QURATOR and SPEAKER.