Jana Götze

Also published as: Jana Goetze


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The slurk Interaction Server Framework: Better Data for Better Dialog Models
Jana Götze | Maike Paetzel-Prüsmann | Wencke Liermann | Tim Diekmann | David Schlangen
Proceedings of the Thirteenth Language Resources and Evaluation Conference

This paper presents the slurk software, a lightweight interaction server for setting up dialog data collections and running experiments. slurk enables a multitude of settings including text-based, speech and video interaction between two or more humans or humans and bots, and a multimodal display area for presenting shared or private interactive context. The software is implemented in Python with an HTML and JavaScript frontend that can easily be adapted to individual needs. It also provides a setup for pairing participants on common crowdworking platforms such as Amazon Mechanical Turk and some example bot scripts for common interaction scenarios.


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From “Before” to “After”: Generating Natural Language Instructions from Image Pairs in a Simple Visual Domain
Robin Rojowiec | Jana Götze | Philipp Sadler | Henrik Voigt | Sina Zarrieß | David Schlangen
Proceedings of the 13th International Conference on Natural Language Generation

While certain types of instructions can be com-pactly expressed via images, there are situations where one might want to verbalise them, for example when directing someone. We investigate the task of Instruction Generation from Before/After Image Pairs which is to derive from images an instruction for effecting the implied change. For this, we make use of prior work on instruction following in a visual environment. We take an existing dataset, the BLOCKS data collected by Bisk et al. (2016) and investigate whether it is suitable for training an instruction generator as well. We find that it is, and investigate several simple baselines, taking these from the related task of image captioning. Through a series of experiments that simplify the task (by making image processing easier or completely side-stepping it; and by creating template-based targeted instructions), we investigate areas for improvement. We find that captioning models get some way towards solving the task, but have some difficulty with it, and future improvements must lie in the way the change is detected in the instruction.


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SpaceRef: A corpus of street-level geographic descriptions
Jana Götze | Johan Boye
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)

This article describes SPACEREF, a corpus of street-level geographic descriptions. Pedestrians are walking a route in a (real) urban environment, describing their actions. Their position is automatically logged, their speech is manually transcribed, and their references to objects are manually annotated with respect to a crowdsourced geographic database. We describe how the data was collected and annotated, and how it has been used in the context of creating resources for an automatic pedestrian navigation system.


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Resolving Spatial References using Crowdsourced Geographical Data
Jana Götze | Johan Boye
Proceedings of the 20th Nordic Conference of Computational Linguistics (NODALIDA 2015)


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Proceedings of the EACL 2014 Workshop on Dialogue in Motion
Tiphaine Dalmas | Jana Götze | Joakim Gustafson | Srinivasan Janarthanam | Jan Kleindienst | Christian Mueller | Amanda Stent | Andreas Vlachos
Proceedings of the EACL 2014 Workshop on Dialogue in Motion


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Deriving Salience Models from Human Route Directions
Jana Götze | Johan Boye
Proceedings of the IWCS 2013 Workshop on Computational Models of Spatial Language Interpretation and Generation (CoSLI-3)


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Integrating Location, Visibility, and Question-Answering in a Spoken Dialogue System for Pedestrian City Exploration
Srinivasan Janarthanam | Oliver Lemon | Xingkun Liu | Phil Bartie | William Mackaness | Tiphaine Dalmas | Jana Goetze
Proceedings of the 13th Annual Meeting of the Special Interest Group on Discourse and Dialogue