Robert Weißgraeber


2022

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Data-to-text systems as writing environment
Adela Schneider | Andreas Madsack | Johanna Heininger | Ching-Yi Chen | Robert Weißgraeber
Proceedings of the First Workshop on Intelligent and Interactive Writing Assistants (In2Writing 2022)

Today, data-to-text systems are used as commercial solutions for automated text productionof large quantities of text. Therefore, they already represent a new technology of writing.This new technology requires the author, asan act of writing, both to configure a systemthat then takes over the transformation into areal text, but also to maintain strategies of traditional writing. What should an environmentlook like, where a human guides a machineto write texts? Based on a comparison of theNLG pipeline architecture with the results ofthe research on the human writing process, thispaper attempts to take an overview of whichtasks need to be solved and which strategiesare necessary to produce good texts in this environment. From this synopsis, principles for thedesign of data-to-text systems as a functioningwriting environment are then derived.

2019

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AX Semantics’ Submission to the SIGMORPHON 2019 Shared Task
Andreas Madsack | Robert Weißgraeber
Proceedings of the 16th Workshop on Computational Research in Phonetics, Phonology, and Morphology

This paper describes the AX Semantics’ submission to the SIGMORPHON 2019 shared task on morphological reinflection. We implemented two systems, both tackling the task for all languages in one codebase, without any underlying language specific features. The first one is an encoder-decoder model using AllenNLP; the second system uses the same model modified by a custom trainer that trains only with the target language resources after a specific threshold. We especially focused on building an implementation using AllenNLP with out-of-the-box methods to facilitate easy operation and reuse.

2018

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AX Semantics’ Submission to the Surface Realization Shared Task 2018
Andreas Madsack | Johanna Heininger | Nyamsuren Davaasambuu | Vitaliia Voronik | Michael Käufl | Robert Weißgraeber
Proceedings of the First Workshop on Multilingual Surface Realisation

In this paper we describe our system and experimental results on the development set of the Surface Realisation Shared Task. Our system is an entry for the Shallow-Task, with two different models based on deep-learning implementations for building the sentence combined with a rule-based morphology component.

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Multi-Language Surface Realisation as REST API based NLG Microservice
Andreas Madsack | Johanna Heininger | Nyamsuren Davaasambuu | Vitaliia Voronik | Michael Käufl | Robert Weißgraeber
Proceedings of the 11th International Conference on Natural Language Generation

We present a readily available API that solves the morphology component for surface realizers in 10 languages (e.g., English, German and Finnish) for any topic and is available as REST API. This can be used to add morphology to any kind of NLG application (e.g., a multi-language chatbot), without requiring computational linguistic knowledge by the integrator.

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AX Semantics’ Submission to the CoNLLSIGMORPHON 2018 Shared Task
Andreas Madsack | Alessia Cavallo | Johanna Heininger | Robert Weißgraeber
Proceedings of the CoNLL–SIGMORPHON 2018 Shared Task: Universal Morphological Reinflection

2017

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A working, non-trivial, topically indifferent NLG System for 17 languages
Robert Weißgraeber | Andreas Madsack
Proceedings of the 10th International Conference on Natural Language Generation

A fully fledged practical working application for a rule-based NLG system is presented that is able to create non-trivial, human sounding narrative from structured data, in any language and for any topic.