@inproceedings{26049,
  abstract     = {{Content is the new oil. Users consume billions of terabytes a day while surfing on news sites or blogs, posting on social media sites, and sending chat messages around the globe. While content is heterogeneous, the dominant form of web content is text. There are situations where more diversity needs to be introduced into text content, for example, to reuse it on websites or to allow a chatbot to base its models on the information conveyed rather than of the language used. In order to achieve this, paraphrasing techniques have been developed: One example is Text spinning, a technique that automatically paraphrases text while leaving the intent intact. This makes it easier to reuse content, or to change the language generated by the bot more human. One method for modifying texts is a combination of translation and back-translation. This paper presents NATTS, a naive approach that uses transformer-based translation models to create diversified text, combining translation steps in one model. An advantage of this approach is that it can be fine-tuned and handle technical language.}},
  author       = {{Bäumer, Frederik Simon and Kersting, Joschka and Denisov, Sergej and Geierhos, Michaela}},
  booktitle    = {{PROCEEDINGS OF THE INTERNATIONAL CONFERENCES ON WWW/INTERNET 2021 AND APPLIED COMPUTING 2021}},
  keywords     = {{Software Requirements, Natural Language Processing, Transfer Learning, On-The-Fly Computing}},
  location     = {{Lisbon, Portugal}},
  pages        = {{221----225}},
  publisher    = {{IADIS}},
  title        = {{{IN OTHER WORDS: A NAIVE APPROACH TO TEXT SPINNING}}},
  year         = {{2021}},
}

@inproceedings{18686,
  author       = {{Kersting, Joschka and Bäumer, Frederik Simon}},
  booktitle    = {{PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON APPLIED COMPUTING 2020}},
  keywords     = {{Software Requirements, Natural Language Processing, Transfer Learning, On-The-Fly Computing}},
  location     = {{Lisbon, Portugal}},
  pages        = {{119----123}},
  publisher    = {{IADIS}},
  title        = {{{SEMANTIC TAGGING OF REQUIREMENT DESCRIPTIONS: A TRANSFORMER-BASED APPROACH}}},
  year         = {{2020}},
}

@article{8424,
  abstract     = {{The vision of On-the-Fly (OTF) Computing is to compose and provide software services ad hoc, based on requirement descriptions in natural language. Since non-technical users write their software requirements themselves and in unrestricted natural language, deficits occur such as inaccuracy and incompleteness. These deficits are usually met by natural language processing methods, which have to face special challenges in OTF Computing because maximum automation is the goal. In this paper, we present current automatic approaches for solving inaccuracies and incompletenesses in natural language requirement descriptions and elaborate open challenges. In particular, we will discuss the necessity of domain-specific resources and show why, despite far-reaching automation, an intelligent and guided integration of end users into the compensation process is required. In this context, we present our idea of a chat bot that integrates users into the compensation process depending on the given circumstances. }},
  author       = {{Bäumer, Frederik Simon and Kersting, Joschka and Geierhos, Michaela}},
  issn         = {{2073-431X}},
  journal      = {{Computers}},
  keywords     = {{Inaccuracy Detection, Natural Language Software Requirements, Chat Bot}},
  location     = {{Vilnius, Lithuania}},
  number       = {{1}},
  publisher    = {{MDPI AG, Basel, Switzerland}},
  title        = {{{Natural Language Processing in OTF Computing: Challenges and the Need for Interactive Approaches}}},
  doi          = {{10.3390/computers8010022}},
  volume       = {{8}},
  year         = {{2019}},
}

@inproceedings{4339,
  abstract     = {{On-The-Fly Computing is the vision of covering software needs of end users by fully-automatic compositions of existing software services. End users will receive so-called service compositions tailored to their very individual needs, based on natural language software descriptions. This everyday language may contain inaccuracies and incompleteness, which are well-known challenges in requirements engineering. In addition to existing approaches that try to automatically identify and correct these deficits, there are also new trends to involve users more in the elaboration and refinement process. In this paper, we present the relevant state of the art in the field of automated detection and compensation of multiple inaccuracies in natural language service descriptions and name open challenges needed to be tackled in NL-based software service composition. }},
  author       = {{Bäumer, Frederik Simon and Geierhos, Michaela}},
  booktitle    = {{Proceedings of the 24th International Conference on Information and Software Technologies (ICIST 2018)}},
  editor       = {{Damaševičius, Robertas and Vasiljevienė, Giedrė}},
  isbn         = {{9783319999715}},
  issn         = {{1865-0929}},
  keywords     = {{Inaccuracy detection, Natural language software requirements}},
  location     = {{Vilnius, Lithuania}},
  pages        = {{559--570}},
  publisher    = {{Springer}},
  title        = {{{NLP in OTF Computing: Current Approaches and Open Challenges}}},
  doi          = {{10.1007/978-3-319-99972-2_46}},
  volume       = {{920}},
  year         = {{2018}},
}

@inproceedings{44,
  abstract     = {{Natural language software requirements descriptions enable end users to formulate their wishes and expectations for a future software product without much prior knowledge in requirements engineering. However, these descriptions are susceptible to linguistic inaccuracies such as ambiguities and incompleteness that can harm the development process. There is a number of software solutions that can detect deficits in requirements descriptions and partially solve them, but they are often hard to use and not suitable for end users. For this reason, we develop a software system that helps end-users to create unambiguous and complete requirements descriptions by combining existing expert tools and controlling them using automatic compensation strategies. In order to recognize the necessity of individual compensation methods in the descriptions, we have developed linguistic indicators, which we present in this paper. Based on these indicators, the whole text analysis pipeline is ad-hoc configured and thus adapted to the individual circumstances of a requirements description.}},
  author       = {{Bäumer, Frederik Simon and Geierhos, Michaela}},
  booktitle    = {{Proceedings of the 51st Hawaii International Conference on System Sciences}},
  isbn         = {{978-0-9981331-1-9}},
  keywords     = {{Software Product Lines: Engineering, Services, and Management, Ambiguities, Incompleteness, Natural Language Processing, Software Requirements}},
  location     = {{Big Island, Waikoloa Village}},
  pages        = {{5746--5755}},
  title        = {{{Flexible Ambiguity Resolution and Incompleteness Detection in Requirements Descriptions via an Indicator-based Configuration of Text Analysis Pipelines}}},
  doi          = {{10125/50609}},
  year         = {{2018}},
}